Essays on Empirical Matching Systems

Abstract

This dissertation is a collection of three papers on empirical methods in matching systems. In Chapter 1, I study the estimation of treatment effects in the context of randomized controlled trials conducted with participants in matching systems. I show how conventional methods fail to account for the interference across outcomes induced by matching systems, and therefore yield invalid estimates of causal parameters. I propose a method that solves the interference problem and apply it in two empirical settings. Chapter 2 studies the relevance of the configuration of on- and off-platform options when centralized matching systems operate alongside a decentralized matching process. In these situations, the existence of off-platform options in a decentralized system can affect the outcomes of participants in the centralized system who seek to be matched to on-platform options. We show this by developing and estimating a structural model that considers the interplay between on- and off-platform options in a matching system. Chapter 3 studies the causal effects of different screening and recruiting policies affecting applicants in the Chilean centralized college match. We show how machine learning methods can enhance these screening and recruiting policies.