Heterogeneous Treatment Effects: Instrumental Variables Without Monotonicity?

No 2008-45, Discussion Paper from Tilburg University, Center for Economic Research

Abstract: 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.

JEL-codes: C21 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2008
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Related works:
Working Paper: Heterogeneous Treatment Effects: Instrumental Variables without Monotonicity? (2007)
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