Identification in Endogenous Sequential Treatment Regimes

2023/11/30 by Pedro Picchetti, Picchetti, Pedro
Economics, Econometrics and Finance · Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #Econometrics (econ.EM) #Economic Policies and Impacts #FOS: Computer and information sciences #FOS: Economics and business #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.2311.18555

openalex publication_date 2023/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper develops a novel nonparametric identification method for treatment effects in settings where individuals self-select into treatment sequences. I propose an identification strategy which relies on a dynamic version of standard Instrumental Variables (IV) assumptions and builds on a dynamic version of the Marginal Treatment Effects (MTE) as the fundamental building block for treatment effects. The main contribution of the paper is to relax assumptions on the support of the observed variables and on unobservable gains of treatment that are present in the dynamic treatment effects literature. Monte Carlo simulation studies illustrate the desirable finite-sample performance of a sieve estimator for MTEs and Average Treatment Effects (ATEs) on a close-to-application simulation study.

Related

• Identification in Nonparametric Models for Dynamic Treatment Effects
• Instrumental Variable Estimation of Dynamic Treatment Effects on a Duration Outcome
• Identification of Treatment Effects under Limited Exogenous Variation
• The Instrumental Variable Method for Estimating Local Average Treatment Regime Effects
• Partial Identification of Marginal Treatment Effects with discrete instruments and misreported treatment