Causal Inference in Matching Markets: Simulable Mechanisms

22 Pages Posted: 22 Jan 2020

See all articles by Jiafeng Chen

Jiafeng Chen

Harvard University, Faculty of Arts and Sciences, Students; Harvard University, Harvard College, Students

Date Written: December 29, 2019

Abstract

We formalize an econometric model for two-sided matching mechanisms in a school choice context, where exogenous variation is generated by using lotteries as a tie-breaking mechanism. Our model accommodates a wide range of matching algorithms studied in the theoretical market design literature. We propose a Horvitz–Thompson estimator for the average treatment effect that is exactly unbiased, compatible with multiple treatments, and compatible with heterogeneous treatment effects. We present theoretical properties of the estimator and inference procedures. Our work clarifies the econometric model used in Abdulkadiroğlu et al. (2017) and provides a robustness check on their results.

Keywords: Horvitz-Thompson, Matching, Market Design, Econometrics, Propensity score

JEL Classification: C10, I20, D47

Suggested Citation

Chen, Jiafeng, Causal Inference in Matching Markets: Simulable Mechanisms (December 29, 2019). Available at SSRN: https://ssrn.com/abstract=3510903 or http://dx.doi.org/10.2139/ssrn.3510903

Jiafeng Chen (Contact Author)

Harvard University, Faculty of Arts and Sciences, Students ( email )

Cambridge, MA
United States

Harvard University, Harvard College, Students ( email )

Cambridge, MA
United States

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