Causal Analysis Action Set

Provides actions for statistical causal analysis and causal effect estimation

caEffect Action

Provides model-agnostic methods of estimating potential outcome means and causal effects of categorical treatments..

CASL Syntax

causalanalysis.caEffect <result=results> <status=rc> /
alpha=double,
difference={{
evtLev={"string-1" <, "string-2", ...>} | {double-1 <, double-2, ...>},
refLev={"string-1" <, "string-2", ...>} | {double-1 <, double-2, ...>}
}, {...}},
display={
caseSensitive=TRUE | FALSE,
exclude=TRUE | FALSE,
excludeAll=TRUE | FALSE,
keyIsPath=TRUE | FALSE,
names={"string-1" <, "string-2", ...>},
pathType="LABEL" | "NAME",
traceNames=TRUE | FALSE
},
freq="variable-name",
inference=TRUE | FALSE,
method="AIPW" | "IPW" | "REGADJ" | "TMLE",
outcomeModel={
required parameter predName="string",
required parameter restore={
caslib="string"
dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}
required parameter name="table-name"
whereTable={
casLib="string"
dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}
required parameter name="table-name"
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}}
where="where-expression"
}
}
},
outcomeVar={
countMissing=TRUE | FALSE,
event="string" | double,
levelizeRaw=TRUE | FALSE,
maxLev=integer,
name="variable-name",
trial="variable-name",
type="BINOMIAL" | "CATEGORICAL" | "CONTINUOUS"
},
outputTables={
groupByVarsRaw=TRUE | FALSE,
includeAll=TRUE | FALSE,
names={"string-1" <, "string-2", ...>} | {key-1={casouttable-1} <, key-2={casouttable-2}, ...>},
repeated=TRUE | FALSE,
replace=TRUE | FALSE
},
required parameter pom={{
predOut="variable-name",
required parameter trtLev="string" | double,
trtProb="variable-name"
}, {...}},
pomCov=TRUE | FALSE,
pomInfo=TRUE | FALSE,
scaledIPWFlag=double,
required parameter table={
caslib="string",
computedOnDemand=TRUE | FALSE,
computedVars={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
computedVarsProgram="string",
dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>},
groupBy={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
groupByMode="NOSORT" | "REDISTRIBUTE",
importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters},
required parameter name="table-name",
onDemand=TRUE | FALSE,
orderBy={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
singlePass=TRUE | FALSE,
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
where="where-expression",
whereTable={
casLib="string"
dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}
required parameter name="table-name"
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}}
where="where-expression"
}
},
required parameter treatVar={
condEvent="string" | double,
countMissing=TRUE | FALSE,
levelizeRaw=TRUE | FALSE,
maxLev=integer,
required parameter name="variable-name"
},
weight="variable-name"
;
indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables

Parameter

Subparameter

Description

 outcomeModel

required parameterrestore

specifies the model to use for scoring predicted counterfactual outcomes.

required parametertable

—

specifies the input data table.
Parameters for Creating Output Tables

Parameter

Subparameter

Description

 outputTables

names

lists the names of results tables to save as CAS tables on the server.

Parameter Descriptions

alpha=double

specifies the significance level for the construction of all confidence intervals.

Default 0.05
Range (0, 0.5)

difference={{caEffect_pomComp-1} <, {caEffect_pomComp-2}, ...>}

specifies causal effects to estimate on the difference scale.

The caEffect_pomComp value can be one or more of the following:

evtLev={"string-1" <, "string-2", ...>} | {double-1 <, double-2, ...>}

specifies a list of event levels for causal effect computations.

Alias eventLevel
refLev={"string-1" <, "string-2", ...>} | {double-1 <, double-2, ...>}

specifies a list of reference levels for causal effect computations.

Alias referenceLevel

display={displayTables}

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

inference=TRUE | FALSE

when set to True, computes standard errors and confidence intervals for the potential outcome mean and causal effect estimates.

Default FALSE

method="AIPW" | "IPW" | "REGADJ" | "TMLE"

specifies the method to use for estimating potential outcome means (POMs).

AIPW

estimates POMs by augmented inverse probability weighting. This method requires the observed values of the outcome, predicted treatment probabilities, and predicted counterfactual outcome values.

IPW

estimates POMs by inverse probability weighting. This method requires the observed values of the outcome and predicted treatment probabilities.

REGADJ

estimates POMs by regression adjustment. This method requires predicted counterfactual outcome values.

TMLE

estimates POMs by targeted maximum likelihood estimation. This method requires the observed values of the outcome, predicted treatment probabilities, and predicted counterfactual outcome values.

outcomeModel={caEffect_outcomeModel}

specifies the model to use for scoring predicted counterfactual outcomes.

Alias outModel

The caEffect_outcomeModel value can be one or more of the following:

* predName="string"

specifies the variable created by the saved outcome model that contains the predicted outcome value of interest. The variable typically has a P_ prefix.

Alias predTarget
* restore={castable}

specifies the outcome model in a binary table object that is used to score predicted counterfactual outcomes.

Aliases rstore
store

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies data source options.

Aliases options
dataSource
* name="table-name"

specifies the name of the input table.

whereTable={groupbytable}

specifies an input table that contains rows to use as a WHERE filter. If the vars parameter is not specified, then all the variable names that are common to the input table and the filtering table are used to find matching rows. If the where parameter for the input table and this parameter are specified, then this filtering table is applied first.

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases options
dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

outcomeVar={caEffect_outcomeInfo}

specifies information about the outcome variable.

Aliases outcomeVariable
outVar
Long form outcomeVar={name="variable-name"}
Shortcut form outcomeVar="variable-name"

The caEffect_outcomeInfo value can be one or more of the following:

countMissing=TRUE | FALSE

when set to True, treats missing as a valid level.

Default FALSE
event="string" | double

specifies the event level of a categorical outcome that is modeled.

levelizeRaw=TRUE | FALSE

when set to True, bases the levelization of the outcome variable on raw values.

Default FALSE
maxLev=integer

specifies the maximum number of levels for the outcome variable. A value of 0 means an unlimited number of levels.

Default 0
Minimum value 0
name="variable-name"

specifies the outcome variable. For binomial models, the specified variable is the event variable in the event/trial syntax.

Aliases outcomeVar
depVar
target
trial="variable-name"

specifies the name of a positive numeric variable that is the number of trials for binomial models. When you specify a trial variable, the outcome variable is called the events variable, and it must contain the number of positive responses (or events).

type="BINOMIAL" | "CATEGORICAL" | "CONTINUOUS"

specifies the type of outcome.

BINOMIAL

specifies a binomial outcome. For a binomial outcome, the event probability is modeled, and predicted outcome values must be between 0 and 1, inclusive. If the specified estimation method requires the observed outcome values, you must use event/trial syntax. You specify the event variable by using the name subparameter, and you specify the trial variable by using the trail subparameter. Event variable values must be nonnegative, trial variable values must be positive, and the number of events must be less than or equal to the number of trials.

CATEGORICAL

specifies a categorical outcome. For a categorical outcome, the probability of a designated event level is modeled, and the observed outcome and predicted outcome values must be between 0 and 1, inclusive. If the specified estimation method requires the observed outcome values, you must specify the event level by using the event subparameter.

CONTINUOUS

specifies a continuous outcome. For a continuous outcome, the observed outcome and predicted outcome values can be any real values.

outputTables={outputTables}

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias displayOut

* pom={{caEffect_pomSpec-1} <, {caEffect_pomSpec-2}, ...>}

specifies the potential outcomes to estimate.

Alias pomSpec

The caEffect_pomSpec value can be one or more of the following:

predOut="variable-name"

specifies the input variable that contains the predicted counterfactual outcome values for the specified treatment level.

Alias predOutcome
* trtLev="string" | double

specifies the level of the treatment variable that defines the potential outcome.

Aliases treatmentLevel
level
lev
trtProb="variable-name"

specifies the input variable that contains the predicted probability of having the specified treatment level.

Alias trtLevProb

pomCov=TRUE | FALSE

when set to True, displays a covariance matrix of the potential outcome mean estimates.

Default FALSE

pomInfo=TRUE | FALSE

when set to True, creates a table that summarizes the potential outcome specifications.

Default FALSE

scaledIPWFlag=double

specifies a multiple of the expected inverse probability weight of an observation that is used to flag observations that have large weights.

Default 10
Minimum value (exclusive) 0

* table={castable}

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 2) parameter (Appendix A: Common Parameters).

* treatVar={caEffect_treatInfo}

specifies information about the treatment variable.

Aliases treatmentVariable
trtVar
treatmentVar
treatVariable
Long form treatVar={name="variable-name"}
Shortcut form treatVar="variable-name"

The caEffect_treatInfo value can be one or more of the following:

condEvent="string" | double

specifies the estimation of conditional potential outcome means and the observed level of treatment to condition on. The level of treatment must appear in a potential outcome specification.

countMissing=TRUE | FALSE

when set to True, treats missing as a valid level.

Default FALSE
levelizeRaw=TRUE | FALSE

when set to True, bases the levelization of the treatment variable on raw values.

Default FALSE
maxLev=integer

specifies the maximum number of levels for the treatment variable. A value of 0 means an unlimited number of levels.

Default 0
Minimum value 0
* name="variable-name"

specifies the name of the treatment variable.

Aliases treatmentVar
trtVar
treatment

weight="variable-name"

names the numeric variable to use for performing a weighted analysis of the data.

caEffect Action

Provides model-agnostic methods of estimating potential outcome means and causal effects of categorical treatments..

Lua Syntax

results, info = s:causalanalysis_caEffect{
alpha=double,
difference={{
evtLev={"string-1" <, "string-2", ...>} | {double-1 <, double-2, ...>},
refLev={"string-1" <, "string-2", ...>} | {double-1 <, double-2, ...>}
}, {...}},
display={
caseSensitive=true | false,
exclude=true | false,
excludeAll=true | false,
keyIsPath=true | false,
names={"string-1" <, "string-2", ...>},
pathType="LABEL" | "NAME",
traceNames=true | false
},
freq="variable-name",
inference=true | false,
method="AIPW" | "IPW" | "REGADJ" | "TMLE",
outcomeModel={
required parameter predName="string",
required parameter restore={
caslib="string"
dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}
required parameter name="table-name"
whereTable={
casLib="string"
dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}
required parameter name="table-name"
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}}
where="where-expression"
}
}
},
outcomeVar={
countMissing=true | false,
event="string" | double,
levelizeRaw=true | false,
maxLev=integer,
name="variable-name",
trial="variable-name",
type="BINOMIAL" | "CATEGORICAL" | "CONTINUOUS"
},
outputTables={
groupByVarsRaw=true | false,
includeAll=true | false,
names={"string-1" <, "string-2", ...>} | {key-1={casouttable-1} <, key-2={casouttable-2}, ...>},
repeated=true | false,
replace=true | false
},
required parameter pom={{
predOut="variable-name",
required parameter trtLev="string" | double,
trtProb="variable-name"
}, {...}},
pomCov=true | false,
pomInfo=true | false,
scaledIPWFlag=double,
required parameter table={
caslib="string",
computedOnDemand=true | false,
computedVars={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
computedVarsProgram="string",
dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>},
groupBy={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
groupByMode="NOSORT" | "REDISTRIBUTE",
importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters},
required parameter name="table-name",
onDemand=true | false,
orderBy={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
singlePass=true | false,
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}},
where="where-expression",
whereTable={
casLib="string"
dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}
required parameter name="table-name"
vars={{
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
}, {...}}
where="where-expression"
}
},
required parameter treatVar={
condEvent="string" | double,
countMissing=true | false,
levelizeRaw=true | false,
maxLev=integer,
required parameter name="variable-name"
},
weight="variable-name"
}
indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables

Parameter

Subparameter

Description

 outcomeModel

required parameterrestore

specifies the model to use for scoring predicted counterfactual outcomes.

required parametertable

—

specifies the input data table.
Parameters for Creating Output Tables

Parameter

Subparameter

Description

 outputTables

names

lists the names of results tables to save as CAS tables on the server.

Parameter Descriptions

alpha=double

specifies the significance level for the construction of all confidence intervals.

Default 0.05
Range (0, 0.5)

difference={{caEffect_pomComp-1} <, {caEffect_pomComp-2}, ...>}

specifies causal effects to estimate on the difference scale.

The caEffect_pomComp value can be one or more of the following:

evtLev={"string-1" <, "string-2", ...>} | {double-1 <, double-2, ...>}

specifies a list of event levels for causal effect computations.

Alias eventLevel
refLev={"string-1" <, "string-2", ...>} | {double-1 <, double-2, ...>}

specifies a list of reference levels for causal effect computations.

Alias referenceLevel

display={displayTables}

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

inference=true | false

when set to True, computes standard errors and confidence intervals for the potential outcome mean and causal effect estimates.

Default false

method="AIPW" | "IPW" | "REGADJ" | "TMLE"

specifies the method to use for estimating potential outcome means (POMs).

AIPW

estimates POMs by augmented inverse probability weighting. This method requires the observed values of the outcome, predicted treatment probabilities, and predicted counterfactual outcome values.

IPW

estimates POMs by inverse probability weighting. This method requires the observed values of the outcome and predicted treatment probabilities.

REGADJ

estimates POMs by regression adjustment. This method requires predicted counterfactual outcome values.

TMLE

estimates POMs by targeted maximum likelihood estimation. This method requires the observed values of the outcome, predicted treatment probabilities, and predicted counterfactual outcome values.

outcomeModel={caEffect_outcomeModel}

specifies the model to use for scoring predicted counterfactual outcomes.

Alias outModel

The caEffect_outcomeModel value can be one or more of the following:

* predName="string"

specifies the variable created by the saved outcome model that contains the predicted outcome value of interest. The variable typically has a P_ prefix.

Alias predTarget
* restore={castable}

specifies the outcome model in a binary table object that is used to score predicted counterfactual outcomes.

Aliases rstore
store

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>}

specifies data source options.

Aliases options
dataSource
* name="table-name"

specifies the name of the input table.

whereTable={groupbytable}

specifies an input table that contains rows to use as a WHERE filter. If the vars parameter is not specified, then all the variable names that are common to the input table and the filtering table are used to find matching rows. If the where parameter for the input table and this parameter are specified, then this filtering table is applied first.

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases options
dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

outcomeVar={caEffect_outcomeInfo}

specifies information about the outcome variable.

Aliases outcomeVariable
outVar
Long form outcomeVar={name="variable-name"}
Shortcut form outcomeVar="variable-name"

The caEffect_outcomeInfo value can be one or more of the following:

countMissing=true | false

when set to True, treats missing as a valid level.

Default false
event="string" | double

specifies the event level of a categorical outcome that is modeled.

levelizeRaw=true | false

when set to True, bases the levelization of the outcome variable on raw values.

Default false
maxLev=integer

specifies the maximum number of levels for the outcome variable. A value of 0 means an unlimited number of levels.

Default 0
Minimum value 0
name="variable-name"

specifies the outcome variable. For binomial models, the specified variable is the event variable in the event/trial syntax.

Aliases outcomeVar
depVar
target
trial="variable-name"

specifies the name of a positive numeric variable that is the number of trials for binomial models. When you specify a trial variable, the outcome variable is called the events variable, and it must contain the number of positive responses (or events).

type="BINOMIAL" | "CATEGORICAL" | "CONTINUOUS"

specifies the type of outcome.

BINOMIAL

specifies a binomial outcome. For a binomial outcome, the event probability is modeled, and predicted outcome values must be between 0 and 1, inclusive. If the specified estimation method requires the observed outcome values, you must use event/trial syntax. You specify the event variable by using the name subparameter, and you specify the trial variable by using the trail subparameter. Event variable values must be nonnegative, trial variable values must be positive, and the number of events must be less than or equal to the number of trials.

CATEGORICAL

specifies a categorical outcome. For a categorical outcome, the probability of a designated event level is modeled, and the observed outcome and predicted outcome values must be between 0 and 1, inclusive. If the specified estimation method requires the observed outcome values, you must specify the event level by using the event subparameter.

CONTINUOUS

specifies a continuous outcome. For a continuous outcome, the observed outcome and predicted outcome values can be any real values.

outputTables={outputTables}

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias displayOut

* pom={{caEffect_pomSpec-1} <, {caEffect_pomSpec-2}, ...>}

specifies the potential outcomes to estimate.

Alias pomSpec

The caEffect_pomSpec value can be one or more of the following:

predOut="variable-name"

specifies the input variable that contains the predicted counterfactual outcome values for the specified treatment level.

Alias predOutcome
* trtLev="string" | double

specifies the level of the treatment variable that defines the potential outcome.

Aliases treatmentLevel
level
lev
trtProb="variable-name"

specifies the input variable that contains the predicted probability of having the specified treatment level.

Alias trtLevProb

pomCov=true | false

when set to True, displays a covariance matrix of the potential outcome mean estimates.

Default false

pomInfo=true | false

when set to True, creates a table that summarizes the potential outcome specifications.

Default false

scaledIPWFlag=double

specifies a multiple of the expected inverse probability weight of an observation that is used to flag observations that have large weights.

Default 10
Minimum value (exclusive) 0

* table={castable}

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 2) parameter (Appendix A: Common Parameters).

* treatVar={caEffect_treatInfo}

specifies information about the treatment variable.

Aliases treatmentVariable
trtVar
treatmentVar
treatVariable
Long form treatVar={name="variable-name"}
Shortcut form treatVar="variable-name"

The caEffect_treatInfo value can be one or more of the following:

condEvent="string" | double

specifies the estimation of conditional potential outcome means and the observed level of treatment to condition on. The level of treatment must appear in a potential outcome specification.

countMissing=true | false

when set to True, treats missing as a valid level.

Default false
levelizeRaw=true | false

when set to True, bases the levelization of the treatment variable on raw values.

Default false
maxLev=integer

specifies the maximum number of levels for the treatment variable. A value of 0 means an unlimited number of levels.

Default 0
Minimum value 0
* name="variable-name"

specifies the name of the treatment variable.

Aliases treatmentVar
trtVar
treatment

weight="variable-name"

names the numeric variable to use for performing a weighted analysis of the data.

caEffect Action

Provides model-agnostic methods of estimating potential outcome means and causal effects of categorical treatments..

Python Syntax

results=s.causalanalysis.caEffect(
alpha=double,
difference=[{
"evtLev":["string-1" <, "string-2", ...>] | [double-1 <, double-2, ...>],
"refLev":["string-1" <, "string-2", ...>] | [double-1 <, double-2, ...>]
}<, {...}>],
display={
"caseSensitive":True | False,
"exclude":True | False,
"excludeAll":True | False,
"keyIsPath":True | False,
"names":["string-1" <, "string-2", ...>],
"pathType":"LABEL" | "NAME",
"traceNames":True | False
},
freq="variable-name",
inference=True | False,
method="AIPW" | "IPW" | "REGADJ" | "TMLE",
outcomeModel={
required parameter "predName":"string",
required parameter "restore":{
"caslib":"string"
"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}
required parameter "name":"table-name"
"whereTable":{
"casLib":"string"
"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}
required parameter "name":"table-name"
"vars":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>]
"where":"where-expression"
}
}
},
outcomeVar={
"countMissing":True | False,
"event":"string" | double,
"levelizeRaw":True | False,
"maxLev":integer,
"name":"variable-name",
"trial":"variable-name",
"type":"BINOMIAL" | "CATEGORICAL" | "CONTINUOUS"
},
outputTables={
"groupByVarsRaw":True | False,
"includeAll":True | False,
"names":["string-1" <, "string-2", ...>] | {"key-1":{casouttable-1} <, "key-2":{casouttable-2}, ...>},
"repeated":True | False,
"replace":True | False
},
required parameter pom=[{
"predOut":"variable-name",
required parameter "trtLev":"string" | double,
"trtProb":"variable-name"
}<, {...}>],
pomCov=True | False,
pomInfo=True | False,
scaledIPWFlag=double,
required parameter table={
"caslib":"string",
"computedOnDemand":True | False,
"computedVars":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
"computedVarsProgram":"string",
"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>},
"groupBy":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
"groupByMode":"NOSORT" | "REDISTRIBUTE",
"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters},
required parameter "name":"table-name",
"onDemand":True | False,
"orderBy":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
"singlePass":True | False,
"vars":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>],
"where":"where-expression",
"whereTable":{
"casLib":"string"
"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}
"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}
required parameter "name":"table-name"
"vars":[{
"format":"string",
"formattedLength":integer,
"label":"string",
required parameter "name":"variable-name",
"nfd":integer,
"nfl":integer
}<, {...}>]
"where":"where-expression"
}
},
required parameter treatVar={
"condEvent":"string" | double,
"countMissing":True | False,
"levelizeRaw":True | False,
"maxLev":integer,
required parameter "name":"variable-name"
},
weight="variable-name"
)
indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables

Parameter

Subparameter

Description

 outcomeModel

required parameterrestore

specifies the model to use for scoring predicted counterfactual outcomes.

required parametertable

—

specifies the input data table.
Parameters for Creating Output Tables

Parameter

Subparameter

Description

 outputTables

names

lists the names of results tables to save as CAS tables on the server.

Parameter Descriptions

alpha=double

specifies the significance level for the construction of all confidence intervals.

Default 0.05
Range (0, 0.5)

difference=[{caEffect_pomComp-1} <, {caEffect_pomComp-2}, ...>]

specifies causal effects to estimate on the difference scale.

The caEffect_pomComp value can be one or more of the following:

"evtLev":["string-1" <, "string-2", ...>] | [double-1 <, double-2, ...>]

specifies a list of event levels for causal effect computations.

Alias eventLevel
"refLev":["string-1" <, "string-2", ...>] | [double-1 <, double-2, ...>]

specifies a list of reference levels for causal effect computations.

Alias referenceLevel

display={displayTables}

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

inference=True | False

when set to True, computes standard errors and confidence intervals for the potential outcome mean and causal effect estimates.

Default False

method="AIPW" | "IPW" | "REGADJ" | "TMLE"

specifies the method to use for estimating potential outcome means (POMs).

AIPW

estimates POMs by augmented inverse probability weighting. This method requires the observed values of the outcome, predicted treatment probabilities, and predicted counterfactual outcome values.

IPW

estimates POMs by inverse probability weighting. This method requires the observed values of the outcome and predicted treatment probabilities.

REGADJ

estimates POMs by regression adjustment. This method requires predicted counterfactual outcome values.

TMLE

estimates POMs by targeted maximum likelihood estimation. This method requires the observed values of the outcome, predicted treatment probabilities, and predicted counterfactual outcome values.

outcomeModel={caEffect_outcomeModel}

specifies the model to use for scoring predicted counterfactual outcomes.

Alias outModel

The caEffect_outcomeModel value can be one or more of the following:

* "predName":"string"

specifies the variable created by the saved outcome model that contains the predicted outcome value of interest. The variable typically has a P_ prefix.

Alias predTarget
* "restore":{castable}

specifies the outcome model in a binary table object that is used to score predicted counterfactual outcomes.

Aliases rstore
store

The castable value can be one or more of the following:

"caslib":"string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>}

specifies data source options.

Aliases options
dataSource
* "name":"table-name"

specifies the name of the input table.

"whereTable":{groupbytable}

specifies an input table that contains rows to use as a WHERE filter. If the vars parameter is not specified, then all the variable names that are common to the input table and the filtering table are used to find matching rows. If the where parameter for the input table and this parameter are specified, then this filtering table is applied first.

The groupbytable value can be one or more of the following:

"casLib":"string"

specifies the caslib for the filter table. By default, the active caslib is used.

"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

specifies data source options.

Aliases options
dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters}

specifies the settings for reading a table from a data source.

Alias import_

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* "name":"table-name"

specifies the name of the filter table.

"vars":[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

"format":"string"

specifies the format to apply to the variable.

"formattedLength":integer

specifies the length of the format field plus the length of the format precision.

"label":"string"

specifies the descriptive label for the variable.

* "name":"variable-name"

specifies the name for the variable.

"nfd":integer

specifies the length of the format precision.

"nfl":integer

specifies the length of the format field.

"where":"where-expression"

specifies an expression for subsetting the data from the filter table.

outcomeVar={caEffect_outcomeInfo}

specifies information about the outcome variable.

Aliases outcomeVariable
outVar
Long form outcomeVar={"name":"variable-name"}
Shortcut form outcomeVar="variable-name"

The caEffect_outcomeInfo value can be one or more of the following:

"countMissing":True | False

when set to True, treats missing as a valid level.

Default False
"event":"string" | double

specifies the event level of a categorical outcome that is modeled.

"levelizeRaw":True | False

when set to True, bases the levelization of the outcome variable on raw values.

Default False
"maxLev":integer

specifies the maximum number of levels for the outcome variable. A value of 0 means an unlimited number of levels.

Default 0
Minimum value 0
"name":"variable-name"

specifies the outcome variable. For binomial models, the specified variable is the event variable in the event/trial syntax.

Aliases outcomeVar
depVar
target
"trial":"variable-name"

specifies the name of a positive numeric variable that is the number of trials for binomial models. When you specify a trial variable, the outcome variable is called the events variable, and it must contain the number of positive responses (or events).

"type":"BINOMIAL" | "CATEGORICAL" | "CONTINUOUS"

specifies the type of outcome.

BINOMIAL

specifies a binomial outcome. For a binomial outcome, the event probability is modeled, and predicted outcome values must be between 0 and 1, inclusive. If the specified estimation method requires the observed outcome values, you must use event/trial syntax. You specify the event variable by using the name subparameter, and you specify the trial variable by using the trail subparameter. Event variable values must be nonnegative, trial variable values must be positive, and the number of events must be less than or equal to the number of trials.

CATEGORICAL

specifies a categorical outcome. For a categorical outcome, the probability of a designated event level is modeled, and the observed outcome and predicted outcome values must be between 0 and 1, inclusive. If the specified estimation method requires the observed outcome values, you must specify the event level by using the event subparameter.

CONTINUOUS

specifies a continuous outcome. For a continuous outcome, the observed outcome and predicted outcome values can be any real values.

outputTables={outputTables}

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias displayOut

* pom=[{caEffect_pomSpec-1} <, {caEffect_pomSpec-2}, ...>]

specifies the potential outcomes to estimate.

Alias pomSpec

The caEffect_pomSpec value can be one or more of the following:

"predOut":"variable-name"

specifies the input variable that contains the predicted counterfactual outcome values for the specified treatment level.

Alias predOutcome
* "trtLev":"string" | double

specifies the level of the treatment variable that defines the potential outcome.

Aliases treatmentLevel
level
lev
"trtProb":"variable-name"

specifies the input variable that contains the predicted probability of having the specified treatment level.

Alias trtLevProb

pomCov=True | False

when set to True, displays a covariance matrix of the potential outcome mean estimates.

Default False

pomInfo=True | False

when set to True, creates a table that summarizes the potential outcome specifications.

Default False

scaledIPWFlag=double

specifies a multiple of the expected inverse probability weight of an observation that is used to flag observations that have large weights.

Default 10
Minimum value (exclusive) 0

* table={castable}

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 2) parameter (Appendix A: Common Parameters).

* treatVar={caEffect_treatInfo}

specifies information about the treatment variable.

Aliases treatmentVariable
trtVar
treatmentVar
treatVariable
Long form treatVar={"name":"variable-name"}
Shortcut form treatVar="variable-name"

The caEffect_treatInfo value can be one or more of the following:

"condEvent":"string" | double

specifies the estimation of conditional potential outcome means and the observed level of treatment to condition on. The level of treatment must appear in a potential outcome specification.

"countMissing":True | False

when set to True, treats missing as a valid level.

Default False
"levelizeRaw":True | False

when set to True, bases the levelization of the treatment variable on raw values.

Default False
"maxLev":integer

specifies the maximum number of levels for the treatment variable. A value of 0 means an unlimited number of levels.

Default 0
Minimum value 0
* "name":"variable-name"

specifies the name of the treatment variable.

Aliases treatmentVar
trtVar
treatment

weight="variable-name"

names the numeric variable to use for performing a weighted analysis of the data.

caEffect Action

Provides model-agnostic methods of estimating potential outcome means and causal effects of categorical treatments..

R Syntax

results <– cas.causalanalysis.caEffect(s,
alpha=double,
difference=list( list(
evtLev=list("string-1" <, "string-2", ...>) | list(double-1 <, double-2, ...>),
refLev=list("string-1" <, "string-2", ...>) | list(double-1 <, double-2, ...>)
) <, list(...)>),
display=list(
caseSensitive=TRUE | FALSE,
exclude=TRUE | FALSE,
excludeAll=TRUE | FALSE,
keyIsPath=TRUE | FALSE,
names=list("string-1" <, "string-2", ...>),
pathType="LABEL" | "NAME",
traceNames=TRUE | FALSE
),
freq="variable-name",
inference=TRUE | FALSE,
method="AIPW" | "IPW" | "REGADJ" | "TMLE",
outcomeModel=list(
required parameter predName="string",
required parameter restore=list(
caslib="string"
dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>)
required parameter name="table-name"
whereTable=list(
casLib="string"
dataSourceOptions=list(adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters)
importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters)
required parameter name="table-name"
vars=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>)
where="where-expression"
)
)
),
outcomeVar=list(
countMissing=TRUE | FALSE,
event="string" | double,
levelizeRaw=TRUE | FALSE,
maxLev=integer,
name="variable-name",
trial="variable-name",
type="BINOMIAL" | "CATEGORICAL" | "CONTINUOUS"
),
outputTables=list(
groupByVarsRaw=TRUE | FALSE,
includeAll=TRUE | FALSE,
names=list("string-1" <, "string-2", ...>) | list(key-1=list(casouttable-1) <, key-2=list(casouttable-2), ...>),
repeated=TRUE | FALSE,
replace=TRUE | FALSE
),
required parameter pom=list( list(
predOut="variable-name",
required parameter trtLev="string" | double,
trtProb="variable-name"
) <, list(...)>),
pomCov=TRUE | FALSE,
pomInfo=TRUE | FALSE,
scaledIPWFlag=double,
required parameter table=list(
caslib="string",
computedOnDemand=TRUE | FALSE,
computedVars=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
computedVarsProgram="string",
dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>),
groupBy=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
groupByMode="NOSORT" | "REDISTRIBUTE",
importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters),
required parameter name="table-name",
onDemand=TRUE | FALSE,
orderBy=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
singlePass=TRUE | FALSE,
vars=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>),
where="where-expression",
whereTable=list(
casLib="string"
dataSourceOptions=list(adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters)
importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters)
required parameter name="table-name"
vars=list( list(
format="string",
formattedLength=integer,
label="string",
required parameter name="variable-name",
nfd=integer,
nfl=integer
) <, list(...)>)
where="where-expression"
)
),
required parameter treatVar=list(
condEvent="string" | double,
countMissing=TRUE | FALSE,
levelizeRaw=TRUE | FALSE,
maxLev=integer,
required parameter name="variable-name"
),
weight="variable-name"
)
indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables

Parameter

Subparameter

Description

 outcomeModel

required parameterrestore

specifies the model to use for scoring predicted counterfactual outcomes.

required parametertable

—

specifies the input data table.
Parameters for Creating Output Tables

Parameter

Subparameter

Description

 outputTables

names

lists the names of results tables to save as CAS tables on the server.

Parameter Descriptions

alpha=double

specifies the significance level for the construction of all confidence intervals.

Default 0.05
Range (0, 0.5)

difference=list( list(caEffect_pomComp-1) <, list(caEffect_pomComp-2), ...>)

specifies causal effects to estimate on the difference scale.

The caEffect_pomComp value can be one or more of the following:

evtLev=list("string-1" <, "string-2", ...>) | list(double-1 <, double-2, ...>)

specifies a list of event levels for causal effect computations.

Alias eventLevel
refLev=list("string-1" <, "string-2", ...>) | list(double-1 <, double-2, ...>)

specifies a list of reference levels for causal effect computations.

Alias referenceLevel

display=list(displayTables)

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

freq="variable-name"

names the numeric variable that contains the frequency of occurrence for each observation.

inference=TRUE | FALSE

when set to True, computes standard errors and confidence intervals for the potential outcome mean and causal effect estimates.

Default FALSE

method="AIPW" | "IPW" | "REGADJ" | "TMLE"

specifies the method to use for estimating potential outcome means (POMs).

AIPW

estimates POMs by augmented inverse probability weighting. This method requires the observed values of the outcome, predicted treatment probabilities, and predicted counterfactual outcome values.

IPW

estimates POMs by inverse probability weighting. This method requires the observed values of the outcome and predicted treatment probabilities.

REGADJ

estimates POMs by regression adjustment. This method requires predicted counterfactual outcome values.

TMLE

estimates POMs by targeted maximum likelihood estimation. This method requires the observed values of the outcome, predicted treatment probabilities, and predicted counterfactual outcome values.

outcomeModel=list(caEffect_outcomeModel)

specifies the model to use for scoring predicted counterfactual outcomes.

Alias outModel

The caEffect_outcomeModel value can be one or more of the following:

* predName="string"

specifies the variable created by the saved outcome model that contains the predicted outcome value of interest. The variable typically has a P_ prefix.

Alias predTarget
* restore=list(castable)

specifies the outcome model in a binary table object that is used to score predicted counterfactual outcomes.

Aliases rstore
store

The castable value can be one or more of the following:

caslib="string"

specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Specify a value only if you need to access a table from a different caslib.

dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>)

specifies data source options.

Aliases options
dataSource
* name="table-name"

specifies the name of the input table.

whereTable=list(groupbytable)

specifies an input table that contains rows to use as a WHERE filter. If the vars parameter is not specified, then all the variable names that are common to the input table and the filtering table are used to find matching rows. If the where parameter for the input table and this parameter are specified, then this filtering table is applied first.

The groupbytable value can be one or more of the following:

casLib="string"

specifies the caslib for the filter table. By default, the active caslib is used.

dataSourceOptions=list(adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | informix-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sapiq-parameters | sforce-parameters | singlestore_standard-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters)

specifies data source options.

Aliases options
dataSource

For more information about specifying the dataSourceOptions parameter, see the common dataSourceOptions parameter (Appendix A: Common Parameters).

importOptions=list(fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters)

specifies the settings for reading a table from a data source.

Alias import

For more information about specifying the importOptions parameter, see the common importOptions parameter (Appendix A: Common Parameters).

* name="table-name"

specifies the name of the filter table.

vars=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

specifies the variable names to use from the filter table.

The casinvardesc value can be one or more of the following:

format="string"

specifies the format to apply to the variable.

formattedLength=integer

specifies the length of the format field plus the length of the format precision.

label="string"

specifies the descriptive label for the variable.

* name="variable-name"

specifies the name for the variable.

nfd=integer

specifies the length of the format precision.

nfl=integer

specifies the length of the format field.

where="where-expression"

specifies an expression for subsetting the data from the filter table.

outcomeVar=list(caEffect_outcomeInfo)

specifies information about the outcome variable.

Aliases outcomeVariable
outVar
Long form outcomeVar=list(name="variable-name")
Shortcut form outcomeVar="variable-name"

The caEffect_outcomeInfo value can be one or more of the following:

countMissing=TRUE | FALSE

when set to True, treats missing as a valid level.

Default FALSE
event="string" | double

specifies the event level of a categorical outcome that is modeled.

levelizeRaw=TRUE | FALSE

when set to True, bases the levelization of the outcome variable on raw values.

Default FALSE
maxLev=integer

specifies the maximum number of levels for the outcome variable. A value of 0 means an unlimited number of levels.

Default 0
Minimum value 0
name="variable-name"

specifies the outcome variable. For binomial models, the specified variable is the event variable in the event/trial syntax.

Aliases outcomeVar
depVar
target
trial="variable-name"

specifies the name of a positive numeric variable that is the number of trials for binomial models. When you specify a trial variable, the outcome variable is called the events variable, and it must contain the number of positive responses (or events).

type="BINOMIAL" | "CATEGORICAL" | "CONTINUOUS"

specifies the type of outcome.

BINOMIAL

specifies a binomial outcome. For a binomial outcome, the event probability is modeled, and predicted outcome values must be between 0 and 1, inclusive. If the specified estimation method requires the observed outcome values, you must use event/trial syntax. You specify the event variable by using the name subparameter, and you specify the trial variable by using the trail subparameter. Event variable values must be nonnegative, trial variable values must be positive, and the number of events must be less than or equal to the number of trials.

CATEGORICAL

specifies a categorical outcome. For a categorical outcome, the probability of a designated event level is modeled, and the observed outcome and predicted outcome values must be between 0 and 1, inclusive. If the specified estimation method requires the observed outcome values, you must specify the event level by using the event subparameter.

CONTINUOUS

specifies a continuous outcome. For a continuous outcome, the observed outcome and predicted outcome values can be any real values.

outputTables=list(outputTables)

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias displayOut

* pom=list( list(caEffect_pomSpec-1) <, list(caEffect_pomSpec-2), ...>)

specifies the potential outcomes to estimate.

Alias pomSpec

The caEffect_pomSpec value can be one or more of the following:

predOut="variable-name"

specifies the input variable that contains the predicted counterfactual outcome values for the specified treatment level.

Alias predOutcome
* trtLev="string" | double

specifies the level of the treatment variable that defines the potential outcome.

Aliases treatmentLevel
level
lev
trtProb="variable-name"

specifies the input variable that contains the predicted probability of having the specified treatment level.

Alias trtLevProb

pomCov=TRUE | FALSE

when set to True, displays a covariance matrix of the potential outcome mean estimates.

Default FALSE

pomInfo=TRUE | FALSE

when set to True, creates a table that summarizes the potential outcome specifications.

Default FALSE

scaledIPWFlag=double

specifies a multiple of the expected inverse probability weight of an observation that is used to flag observations that have large weights.

Default 10
Minimum value (exclusive) 0

* table=list(castable)

specifies the input data table.

For more information about specifying the table parameter, see the common castable (Form 2) parameter (Appendix A: Common Parameters).

* treatVar=list(caEffect_treatInfo)

specifies information about the treatment variable.

Aliases treatmentVariable
trtVar
treatmentVar
treatVariable
Long form treatVar=list(name="variable-name")
Shortcut form treatVar="variable-name"

The caEffect_treatInfo value can be one or more of the following:

condEvent="string" | double

specifies the estimation of conditional potential outcome means and the observed level of treatment to condition on. The level of treatment must appear in a potential outcome specification.

countMissing=TRUE | FALSE

when set to True, treats missing as a valid level.

Default FALSE
levelizeRaw=TRUE | FALSE

when set to True, bases the levelization of the treatment variable on raw values.

Default FALSE
maxLev=integer

specifies the maximum number of levels for the treatment variable. A value of 0 means an unlimited number of levels.

Default 0
Minimum value 0
* name="variable-name"

specifies the name of the treatment variable.

Aliases treatmentVar
trtVar
treatment

weight="variable-name"

names the numeric variable to use for performing a weighted analysis of the data.

Last updated: March 05, 2026