Heterogeneous Treatment Effects: Instrumental Variables Without Monotonicity?

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A fundamental identification problem in program evaluation arises when idiosyncratic gains from participation and the treatment decision depend on each other. Imbens and Angrist (1994) were the first to exploit a monotonicity condition in order to identify a local average treatment effect parameter using instrumental variables. More recently, Heckman and Vytlacil (1999) suggested estimation of a variety of treatment effect parameters using a local version of their approach. However, identification hinges on the same monotonicity assumption that is fundamentally untestable. We investigate the sensitivity of respective estimates to reasonable departures from monotonicity that are likely to be encountered in practice. Approximations to respective bias terms are derived. In an empirical application the bias is calculated and bias corrected estimates are obtained. The accuracy of the approximation is investigated in a Monte Carlo study.
Original languageEnglish
Place of PublicationTilburg
Number of pages39
Publication statusPublished - 2008

Publication series

NameCentER Discussion Paper


  • Program evaluation
  • heterogeneity
  • identification
  • dummy endogenous variable
  • selection on unobservables
  • instrumental variables
  • monotonicity
  • nonseparable index selection model


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