Treatment Effects With Unobserved Heterogeneity: A Set Identification Approach

Sung Jae Jun, Yoonseok Lee, Youngki Shin

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We propose the sharp identifiable bounds of the potential outcome distributions using panel data. We allow for the possibility that statistical randomization of treatment assignments is not achieved until unobserved heterogeneity is properly controlled for. We use certain stationarity assumptions to obtain the sharp bounds. Our approach allows for dynamic treatment decisions, where the current treatment decisions may depend on the past treatments or the past observed outcomes. As an empirical illustration, we study the effect of smoking during pregnancy on infant birthweight. We find that for the group of switchers the infant birthweight of a smoking mother is first-order stochastically dominated by that of a nonsmoking mother.

Original languageEnglish (US)
Pages (from-to)302-311
Number of pages10
JournalJournal of Business and Economic Statistics
Volume34
Issue number2
DOIs
StatePublished - Apr 2 2016

Keywords

  • Dynamic treatment decisions
  • Panel data
  • Partial identification
  • Stochastic dominance

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

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