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CAUSALTRT Procedure


The CAUSALTRT procedure implements causal inference methods that are designed primarily for use with data from nonrandomized trials or observational studies. The CAUSALTRT procedure provides methods for estimating causal treatment effects controlling for confounding between the outcome and characteristics of the subjects. Specially, the procedure estimates the average causal effect of a binary treatment on a continuous or discrete outcome in nonrandomized trials or observational studies in the presence of confounding variables.

You can adjust for confounding by modeling the treatment assignment or the outcome or both. Modeling the treatment assignment leads to inverse probability weighting methods, and modeling the outcome leads to regression adjustment methods. Modeling both leads to doubly robust methods that can provide unbiased estimates for the treatment effect even if one of the models is misspecified.

The following highlights are available in the CAUSALTRT procedure:

Provides the following methods to estimate causal effects

Estimates two types of causal effects:

Computes standard errors and confidence intervals for the causal effects by the following methods:

Provides the following types of graphical output:

Saves the propensity scores, inverse probability weights, and the predicted potential outcomes in a SAS data set.

Performs BY group processing, which enables you to obtain separate analyses on grouped observations.

Supports the following diagnostics for assessing the balance produced by a propensity score model:

For further details see the SAS/STAT User's Guide: The CAUSALTRT Procedure
( PDF | HTML )

Examples