Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India
The paper develops a generic method to use any machine learning algorithm to draw valid statistical inference about features of heterogeneous treatment effects in randomized experiments. Rather than estimating the conditional average treatment effect function itself (which ML may estimate inconsistently), it targets summary parameters such as the best linear predictor of the effect on ML proxies, sorted average effects across groups, and average characteristics of the most/least affected units, using sample splitting and aggregation over many splits to obtain robust confidence intervals. It illustrates the approach with an immunization study in India.