|
Abstract:
|
Applied statisticians and data scientists are increasingly facing data that come from observational studies rather than randomized experiments. Inferring valid causes from observational data is a growing problem for statistical practitioners in applications ranging from health care to marketing to government policy making. This workshop introduces two SAS/STAT® procedures for estimating causal treatment effects from observational data: The CAUSALTRT procedure estimates binary treatment effects by modeling the treatment variable, outcome variable, or both variables; the PSMATCH procedure performs analysis that is based on propensity scores, assesses covariate balance, and creates output data sets that behave like data from randomized experiments. You can then use the output data sets to estimate treatment effects that have valid causal interpretations. This workshop demonstrates the propensity score matching methods, inverse probability of treatment weighting, and doubly robust methods of these two procedures through examples. It emphasizes techniques that promote sound practice and effective communication, such as graphical assessment of covariate balance. It also gives a brief, high-level account of causal inference issues and the principles that underlie the two procedures. Basic familiarity with generalized linear models is assumed.
|
