Beyond teffects and lateffects: Average and local average treatment effects with covariates in Stata

This presentation introduces three Stata commands for treatment-effect estimation with covariates: teffects2, kappalate, and drlate. The talk combines the underlying econometric ideas with a practical discussion of implementation and empirical use. First, teffects2 extends Stata's teffects by implementing IPW, AIPW, and IPWRA estimators for ATE and ATT with exact-balancing inverse probability tilting weights; it can also be used for ATT in difference-in-differences settings. Under these weights, several estimators that usually differ become numerically identical, simplifying interpretation and practice. Second, kappalate implements normalized weighting estimators of LATE, emphasizing finite-sample properties that matter in applications, including invariance to outcome recoding and advantages under one-sided noncompliance. Third, drlate introduces doubly robust IPWRA estimators of LATE and LATT. Throughout, I compare these commands with Stata's built-in teffects and lateffects.