Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India

70 Pages Posted: 13 Jun 2018 Last revised: 7 Jun 2023

See all articles by Victor Chernozhukov

Victor Chernozhukov

Massachusetts Institute of Technology (MIT) - Department of Economics

Mert Demirer

Massachusetts Institute of Technology (MIT)

Esther Duflo

Massachusetts Institute of Technology (MIT) - Department of Economics; Abdul Latif Jameel Poverty Action Lab (J-PAL); National Bureau of Economic Research (NBER); Centre for Economic Policy Research (CEPR); Bureau for Research and Economic Analysis of Development (BREAD)

Iván Fernández‐Val

Boston University - Department of Economics

Date Written: June 2018

Abstract

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.

Suggested Citation

Chernozhukov, Victor and Demirer, Mert and Duflo, Esther and Fernandez-Val, Ivan, Generic Machine Learning Inference on Heterogeneous Treatment Effects in Randomized Experiments, with an Application to Immunization in India (June 2018). NBER Working Paper No. w24678, Available at SSRN: https://ssrn.com/abstract=3194832

Victor Chernozhukov (Contact Author)

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

50 Memorial Drive
Room E52-262f
Cambridge, MA 02142
United States
617-253-4767 (Phone)
617-253-1330 (Fax)

HOME PAGE: http://www.mit.edu/~vchern/

Mert Demirer

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
50 Memorial Drive
Cambridge, MA 02139-4307
United States

Esther Duflo

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

50 Memorial Drive
Room E52-544
Cambridge, MA 02139
United States
617-258-7013 (Phone)
617-253-6915 (Fax)

Abdul Latif Jameel Poverty Action Lab (J-PAL) ( email )

Cambridge, MA
United States

HOME PAGE: http://www.povertyactionlab.org/

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Centre for Economic Policy Research (CEPR)

London
United Kingdom

Bureau for Research and Economic Analysis of Development (BREAD) ( email )

Duke University
Durham, NC 90097
United States

Ivan Fernandez-Val

Boston University - Department of Economics ( email )

270 Bay State Road
Boston, MA 02215
United States

HOME PAGE: http://people.mit.edu/ivanf

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