Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments
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We propose strategies to estimate and make inference on key features of heterogeneous effects in randomized experiments. These key features include best linear predictors of the effects using machine learning proxies, average effects sorted by impact groups, and average characteristics of most and least impacted units. The approach is valid in high dimensional settings, where the effects are proxied by machine learning methods. We post-process these proxies into the estimates of the key features. Our approach is agnostic about the properties of the machine learning estimators used to produce proxies, and it completely avoids making any strong assumption. Estimation and inference relies on repeated data splitting to avoid overfitting and achieve validity. Our variational inference method is shown to be uniformly valid and quantifies the uncertainty coming from both parameter estimation and data splitting. In essence, we take medians of p-values and medians of confidence intervals, resulting from many different data splits, and then adjust their nominal level to guarantee uniform validity. The inference method could be of substantial independent interest in many machine learning applications. Empirical applications illustrate the use of the approach.
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