Estimation of heterogeneous individual treatment effects with endogenous treatments

TL;DR

This paper develops a two-stage estimation method for individual treatment effects in models with endogenous binary treatments, avoiding ill-posed inverse problems, and demonstrates its effectiveness through simulations and an empirical study on retirement savings.

Contribution

It introduces a novel two-stage approach for estimating heterogeneous treatment effects with endogenous treatments, overcoming previous methodological challenges.

Findings

Some individuals experience negative effects from 401(k) programs

Majority of individuals have positive treatment effects

Method shows good finite sample properties in simulations

Abstract

This paper estimates individual treatment effects in a triangular model with binary--valued endogenous treatments. Following the identification strategy established in Vuong and Xu (2014), we propose a two--stage estimation approach. First, we estimate the counterfactual outcome and hence the individual treatment effect (ITE) for every observational unit in the sample. Second, we estimate the density of individual treatment effects in the population. Our estimation method does not suffer from the ill--posed inverse problem associated with inverting a non--linear functional. Asymptotic properties of the proposed method are established. We study its finite sample properties in Monte Carlo experiments. We also illustrate our approach with an empirical application assessing the effects of 401(k) retirement programs on personal savings. Our results show that there exists a small but statistically significant proportion of individuals who experience negative effects, although the majority of ITEs is positive.