Machine Learning Instrument Variables for Causal Inference

Posted: 6 Apr 2019 Last revised: 15 Nov 2019

See all articles by Amit Gandhi

Amit Gandhi

University of Wisconsin - Madison

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 variables 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. Further, O(√n) consistency and asymptotic normality of our estimators hold under standard regularity 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). Available at SSRN: https://ssrn.com/abstract=3352957

Amit Gandhi

University of Wisconsin - Madison ( email )

716 Langdon Street
Madison, WI 53706-1481
United States

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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