Conditional average treatment-effects estimation using Stata

Session

Treatment effects estimate the causal effects of a treatment on an outcome. The effect may be heterogeneous.  Average treatment effects conditional on a set of variables (CATEs) help us understand heterogeneous treatment effects. By construction, they are useful to evaluate how different treatment-assignment policies affect different groups in the population.

In this talk, we will show how to use Stata 19's new command cate to answer questions such as the following:

1. Are the treatment effects heterogeneous?
2. How do the treatment effects vary with some variables?
3. Do the treatment effects vary across prespecified groups?
4. Are there unknown groups in the data for which treatment effects differ?
5. Which is best among possible treatment-assignment rules?