Methods for causal inference and the estimation of treatment effects have received much attention in recent years. Most of the methodological and applied work focuses on the identification of so-called average treatment effects, possibly restricted to the treated or the untreated. However, treatment effects may vary (hence the averaging), and it can be interesting to analyze the patterns of effect heterogeneity. In this talk, I will present a new command called hte that is used for heterogeneous treatment-effect analysis in Stata. hte first constructs balanced propensity-score strata and, within each stratum, estimates the average treatment effect. hte then tests for a linear trend in effects across the strata. The stratum-specific treatment effects and the estimated linear trend are displayed in a two-way graph. hte results from joint work with Jennie E. Brand (UCLA) and Yu Xie (University of Michigan).