Estimating marginal treatment effects under unobserved group heterogeneity

Tadao Hoshino*, Takahide Yanagi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

This article studies the treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment rules. By using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the treatment choice and outcome equations can be heterogeneous across groups. Under the availability of instrumental variables specific to each group, we show that the MTE for each group can be separately identified. On the basis of our identification result, we propose a two-step semiparametric procedure for estimating the group-wise MTE. We illustrate the usefulness of the proposed method with an application to economic returns to college education.

Original languageEnglish
Pages (from-to)197-216
Number of pages20
JournalJournal of Causal Inference
Volume10
Issue number1
DOIs
Publication statusPublished - 2022 Jan 1

Keywords

  • endogeneity
  • finite mixture
  • instrumental variables
  • marginal treatment effects
  • unobserved heterogeneity

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Estimating marginal treatment effects under unobserved group heterogeneity'. Together they form a unique fingerprint.

Cite this