Using the package hettreatreg to interpret OLS estimates under treatment-effect heterogeneity

This presentation describes hettreatreg, a Stata package to compute diagnostics for linear regression when treatment effects are heterogeneous. Following my recent paper, "Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights" (forthcoming, Review of Economics and Statistics), every OLS estimate of the coefficient on a binary variable ("treatment") in a linear model with additive effects can be represented as a weighted average of two other estimates, corresponding to average treatment effects on the treated (ATT) and untreated (ATU). Surprisingly, the weights on these estimates are inversely related to the proportion of observations in each group. Thus, when there are very few treated (untreated) observations, OLS estimates are similar to those of the ATT (ATU). When the sample is roughly balanced, OLS estimates are similar to those of the average treatment effect (ATE). The package hettreatreg estimates the OLS weights on ATT and ATU, computes the associated model diagnostics, and reports the implicit OLS estimates of ATE, ATT, and ATU. I illustrate the use of hettreatreg with empirical examples.