Introducing the CAUSALGRAPH Procedure for Graphical Causal Model Analysis

Clay Thompson of SAS demonstrates how you can use the CAUSALGRAPH procedure for graphical causal model analysis. There are many practical applications that require causal analysis of data from nonrandomized experiments. Causal graphs are a powerful technique that you can use to handle many of the subtle difficulties that arise from the lack of randomization in these studies. The CAUSALGRAPH procedure (PROC CAUSALGRAPH) is designed to facilitate the use of graphical causal models by providing a convenient combination of grammar and options to articulate model assumptions and build a causal model, test those model assumptions, and assess the identifiability of a causal effect.

Clay Thompson is a research statistician developer who is part of the Advanced Analytics division at SAS.

Content Outline
0:10 – A Simple Causal Model Example
2:13 – The CAUSALGRAPH procedure is new in SAS/STAT 15.1
2:50 – Example 1: Finding Valid Adjustment Sets
7:04 – Example 2: Using Observational Data to Estimate a Causal Effect
9:10 – Summary

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