This talk introduces the basic concepts of causal inference including counterfactuals and potential outcomes. Chuck Huber of STATA Corp. will demonstrate how to use Stata's -teffects- suite of commands to fit causal models using propensity score matching, inverse-probability weighting, regression adjustment, "doubly-robust" estimators that use a combination of inverse-probability weighting with regression adjustment, and nearest-neighbor matching.
