TY  - RPRT
        AU  - Bodory, Hugo
AU  - Camponovo, Lorenzo
AU  - Huber, Martin
AU  - Lechner, Michael
        TI  - The Finite Sample Performance of Inference Methods for Propensity Score Matching and Weighting Estimators
        PY  - 2016/Feb/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 9706
        UR  - https://www.iza.org/index.php/publications/dp9706
        AB  - This paper investigates the finite sample properties of a range of inference methods for propensity score-based matching and weighting estimators frequently applied to evaluate the average treatment effect on the treated. We analyse both asymptotic approximations and bootstrap methods for computing variances and confidence intervals in our simulation design, which is based on large scale labor market data from Germany and varies w.r.t. treatment selectivity, effect heterogeneity, the share of treated, and the sample size. The results suggest that in general, the bootstrap procedures dominate the asymptotic ones in terms of size and power for both matching and weighting estimators. Furthermore, the results are qualitatively quite robust across the various simulation features.
        KW  - matching
KW  - treatment effects
KW  - variance estimation
KW  - inference
KW  - inverse probability weighting
        ER  -