Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments

40 Pages Posted: 13 Jun 2018

See all articles by Victor Chernozhukov

Victor Chernozhukov

Massachusetts Institute of Technology (MIT) - Department of Economics; New Economic School

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 by machine learning methods. We post-process these proxies into the estimates of the key features. Our approach is generic, it can be used in conjunction with penalized methods, deep and shallow neural networks, canonical and new random forests, boosted trees, and ensemble methods. It does not rely on strong assumptions. In particular, we don’t require conditions for consistency of the machine learning methods. Estimation and inference relies on repeated data splitting to avoid overfitting and achieve validity. For inference, 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. This variational inference method is shown to be uniformly valid and quantifies the uncertainty coming from both parameter estimation and data splitting. An empirical application to the impact of micro-credit on economic development illustrates the use of the approach in randomized experiments.

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Suggested Citation

Chernozhukov, Victor and Demirer, Mert and Duflo, Esther and Fernandez-Val, Ivan, Generic Machine Learning Inference on Heterogenous Treatment Effects in Randomized Experiments (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 )

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

New Economic School

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Mert Demirer

Massachusetts Institute of Technology (MIT) ( email )

77 Massachusetts Avenue
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Cambridge, MA 02139-4307
United States

Esther Duflo

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

50 Memorial Drive
Room E52-544
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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)

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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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