# Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments

arXiv: https://arxiv.org/abs/2203.14511 · HTML: https://mlsanity.com/paper/2203.14511/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 28 Mar 2022.

## Builds on (most cited first, 8 of 8)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition](https://mlsanity.com/paper/1707.02641/) (2017)
- [Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects](https://mlsanity.com/paper/1706.09523/) (2017)
- [Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects](https://mlsanity.com/paper/2111.07966/) (2021)
- [Stable discovery of interpretable subgroups via calibration in causal studies](https://mlsanity.com/paper/2008.10109/) (2020)
- [Experimental Evaluation of Individualized Treatment Rules](https://mlsanity.com/paper/1905.05389/) (2019)
- [Randomization Inference for Treatment Effect Variation](https://mlsanity.com/paper/1412.5000/) (2014)
- [Decomposing Treatment Effect Variation](https://mlsanity.com/paper/1605.06566/) (2016)

## Built on by (most cited first, 7 of 7)

- [Evaluating the Effectiveness of Index-Based Treatment Allocation](https://mlsanity.com/paper/2402.11771/) (2024)
- [Fostering the Ecosystem of AI for Social Impact Requires Expanding and Strengthening Evaluation Standards](https://mlsanity.com/paper/2510.18238/) (2025)
- [A Statistical Test for the Benefits of Personalizing Interventions](https://mlsanity.com/paper/2607.08951/) (2026)
- [Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning](https://mlsanity.com/paper/2310.07973/) (2023)
- [Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules](https://mlsanity.com/paper/2404.17019/) (2024)
- [Honesty in Causal Forests: When It Helps and When It Hurts](https://mlsanity.com/paper/2506.13107/) (2025)
- [Target-Weighted Neyman Allocation: Experimental Design for Heterogeneous Treatment Effects under Population Shift](https://mlsanity.com/paper/2608.06512/) (2026)

## Abstract (arXiv)

Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with many covariates and small sample size. In addition, the quantification of estimation uncertainty remains a challenge. We develop a general approach to statistical inference for heterogeneous treatment effects discovered by a generic ML algorithm. We apply the Neyman's repeated sampling framework to a common setting, in which researchers use an ML algorithm to estimate the conditional average treatment effect and then divide the sample into several groups based on the magnitude of the estimated effects. We show how to estimate the average treatment effect within each of these groups, and construct a valid confidence interval. In addition, we develop nonparametric tests of treatment effect homogeneity across groups, and rank-consistency of within-group average treatment effects. The validity of our methodology does not rely on the properties of ML algorithms because it is solely based on the randomization of treatment assignment and random sampling of units. Finally, we generalize our methodology to the cross-fitting procedure by accounting for the additional uncertainty induced by the random splitting of data.