Dealing with limited overlap in estimation of average treatment effects
By Richard K. Crump, V. Hotz, Guido W. Imbens, Oscar A. Mitnik. 2009
150 citations recorded by the source corpus.
Estimation of average treatment effects under unconfounded or ignorable treatment assignment is often hampered by lack of overlap in the covariate distributions between treatment groups. This lack of overlap can lead to imprecise estimates, and can make commonly used estimators sensitive to the choice of specification. In such cases researchers have often used ad hoc methods for trimming the sample. We develop a systematic approach to addressing lack of overlap. We characterize optimal subsamples for which the average treatment effect can be estimated most precisely. Under some conditions, the optimal selection rules depend solely on the propensity score. For a wide range of distributions, a good approximation to the optimal rule is provided by the simple rule of thumb to discard all units with estimated propensity scores outside the range [0.1,0.9].
Subfields: Statistics and Probability
Citations in the corpus
- The State of Applied Econometrics - Causality and Policy Evaluation (2016, neutral): “Thus, procedures such as “trimming” the data to eliminate extreme values of the estimated propensity score (thus changing the estimand as in [Crump et al., 2009]) remain important.”
- Assessing the Incidence and Efficiency of a Prominent Place Based Policy (2013, unknown): “To deal with this failure of overlap in the two distributions we drop tracts with propensity scores less than the minimum value found among the treated units and those with propensity scores greater than 0.9 as recommended in Crump et al. (2009).”
- Site Selection Bias in Program Evaluation (2015, unknown): “Site: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)”
- Semiparametric Minimax Rates (2009, unknown): “A convenient average (see [2] for further discussion) is the variance-weighted average treatment effect, given by τ : = Evar(A|Z)c(Z) E var(A|Z) = E cov(Y, A|Z) E var(A|Z) .”