Machine Learning Instrument Variables for Causal Inference

EC '20: Proceedings of the 21st ACM Conference on Economics and Computation

44 Pages Posted: 6 Apr 2019 Last revised: 11 Jan 2021

See all articles by Amit Gandhi

Amit Gandhi

University of Pennsylvania

Kartik Hosanagar

University of Pennsylvania - Operations & Information Management Department

Amandeep Singh

University of Pennsylvania - The Wharton School

Date Written: March 15, 2019

Abstract

Instrumental variables (IVs) are a commonly used technique for causal inference from observational data. In practice, the variation induced by IVs can be limited, which yields imprecise or biased estimates of causal effects and renders the approach ineffective for policy decisions. We confront this challenge by formulating the problem of constructing instrumental variables from candidate exogenous data as a machine learning problem. We propose a novel algorithm, called MLIV (machine-learned instrumental variables), which allows learning of instruments and causal inference to be simultaneously performed from sample data. We provide formal asymptotic theory and show root-n consistency and asymptotic efficiency of our estimators hold under very general conditions. Simulations and application to real-world data demonstrate that the algorithm is highly effective and significantly improves the performance of causal inference from observational data.

Keywords: Econometrics, Machine Learning, Causal Inference, Empirical Industrial Organization

Suggested Citation

Gandhi, Amit and Hosanagar, Kartik and Singh, Amandeep, Machine Learning Instrument Variables for Causal Inference (March 15, 2019). EC '20: Proceedings of the 21st ACM Conference on Economics and Computation, Available at SSRN: https://ssrn.com/abstract=3352957 or http://dx.doi.org/10.2139/ssrn.3352957

Amit Gandhi

University of Pennsylvania ( email )

Philadelphia, PA 19104
United States

HOME PAGE: http://web.sas.upenn.edu/agandhi/cv/

Kartik Hosanagar

University of Pennsylvania - Operations & Information Management Department ( email )

Philadelphia, PA 19104
United States

Amandeep Singh (Contact Author)

University of Pennsylvania - The Wharton School ( email )

3641 Locust Walk
Philadelphia, PA 19104-6365
United States

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