Victor Chernozhukov, Mert Demirer, Esther Duflo, Iván Fernández-Val
arXiv 13 Dec 2017 · Statistics — Machine Learning · publishedEconometrica (2025) · 178 citations (OpenAlex)
arXiv:1712.04802 · PDF · DOI · OpenAlex · Extracted main text
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 (but not necessarily consistently estimated) by predictive and causal machine learning methods. We post-process these proxies into estimates of the key features. Our approach is generic, it can be used in conjunction with penalized methods, neural networks, random forests, boosted trees, and ensemble methods, both predictive and causal. Estimation and inference are based on repeated data splitting to avoid overfitting and achieve validity. We use quantile aggregation of the results across many potential splits, in particular taking medians of p-values and medians and other quantiles of confidence intervals. We show that quantile aggregation lowers estimation risks over a single split procedure, and establish its principal inferential properties. Finally, our analysis reveals ways to build provably better machine learning proxies through causal learning: we can use the objective functions that we develop to construct the best linear predictors of the effects, to obtain better machine learning proxies in the initial step. We illustrate the use of both inferential tools and causal learners with a randomized field experiment that evaluates a combination of nudges to stimulate demand for immunization in India.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | X Nie and S Wager (2020) Quasi-oracle estimation of heterogeneous treatment effects | 1.000 | 7 | 4 | 100% |
| 2 | Vira Semenova, Matt Goldman, Victor Chernozhukov, and Matt Taddy (2017) Estimation and inference on heterogeneous treatment effects in high-dimensional dynamic panels self | 1.000 | 6 | 3 | 100% |
| 3 | Susan Athey and Guido Imbens (2016) Recursive partitioning for heterogeneous causal effects | 1.000 | 5 | 4 | 100% |
| 4 | Vira Semenova and Victor Chernozhukov (2021) Debiased machine learning of conditional average treatment effects and other causal functions self | 0.950 | 7 | 4 | 86% |
| 5 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2017) Double/debiased machine learning for treatment and structural parameters self | 0.928 | 4 | 3 | 100% |
| 6 | Abhijit Banerjee, Arun Chandrasekhar, Esther Duflo, Suresh Dalpath,… (2021) Inference on winners self | 0.874 | 5 | 2 | 100% |
| 7 | Nicolai Meinshausen, Lukas Meier, and Peter Bühlmann (2009) P-values for high-dimensional regression | 0.874 | 5 | 2 | 100% |
| 8 | Abhijit Banerjee, Arun Chandrasekhar, Esther Duflo, Suresh Dalpath,… (2019) Leveraging the social network amplifies the effectiveness of interventions to stimulate take up of immunization self | 0.811 | 4 | 2 | 100% |
| 9 | Dylan J Foster and Vasilis Syrgkanis (2019) Orthogonal statistical learning | 0.737 | 3 | 2 | 100% |
| 10 | Tengyuan Liang, Alexander Rakhlin, and Karthik Sridharan (2015) Learning with square loss: Localization through offset rademacher complexity | 0.644 | 10 | 2 | 40% |
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