Trimming for Bounds on Treatment Effects with Missing Outcomes

21 Pages Posted: 24 Jun 2002 Last revised: 5 Apr 2021

See all articles by David Lee

David Lee

University of California, Berkeley - Department of Economics; National Bureau of Economic Research (NBER)

Date Written: June 2002

Abstract

Empirical researchers routinely encounter sample selection bias whereby 1) the regressor of interest is assumed to be exogenous, 2) the dependent variable is missing in a potentially non-random manner, 3) the dependent variable is characterized by an unbounded (or very large) support, and 4) it is unknown which variables directly affect sample selection but not the outcome. This paper proposes a simple and intuitive bounding procedure that can be used in this context. The proposed trimming procedure yields the tightest bounds on average treatment effects consistent with the observed data. The key assumption is a monotonicity restriction on how the assignment to treatment effects selection -- a restriction that is implicitly assumed in standard formulations of the sample selection problem.

Suggested Citation

Lee, David S., Trimming for Bounds on Treatment Effects with Missing Outcomes (June 2002). NBER Working Paper No. t0277, Available at SSRN: https://ssrn.com/abstract=316780

David S. Lee (Contact Author)

University of California, Berkeley - Department of Economics ( email )

Berkeley, CA 94720-3880
United States
510-642-4628 (Phone)

National Bureau of Economic Research (NBER)

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

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