Identification in nonparametric models for dynamic treatment effects

Vincent Han*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

Abstract

This paper develops a nonparametric model that represents how sequences of outcomes and treatment choices influence one another in a dynamic manner. In this setting, we are interested in identifying the average outcome for individuals in each period, had a particular treatment sequence been assigned. The identification of this quantity allows us to identify the average treatment effects (ATE’s) and the ATE’s on transitions, as well as the optimal treatment regimes, namely, the regimes that maximize the (weighted) sum of the average potential outcomes, possibly less the cost of the treatments. The main contribution of this paper is to relax the sequential randomization assumption widely used in the biostatistics literature by introducing a flexible choice-theoretic framework for a sequence of endogenous treatments. This framework allows non-compliance of subjects in experimental studies or endogenous treatment decisions in observational settings. We show that the parameters of interest are identified under each period’s exclusion restrictions, which are motivated by, e.g., a sequence of randomized treatment assignments or encouragements and a behavioral/information assumption on agents who receive treatments.
Original languageEnglish
Number of pages16
JournalJournal of Econometrics
Early online date27 Nov 2020
DOIs
Publication statusPublished - 27 Nov 2020

Structured keywords

  • ECON Econometrics

Keywords

  • Dynamic treatment effect
  • Endogenous treatment
  • Average treatment effect
  • Optimal treatment regime
  • Instrumental variable

Fingerprint

Dive into the research topics of 'Identification in nonparametric models for dynamic treatment effects'. Together they form a unique fingerprint.

Cite this