TY  - RPRT
        AU  - Frölich, Markus
AU  - Huber, Martin
AU  - Wiesenfarth, Manuel
        TI  - The Finite Sample Performance of Semi- and Nonparametric Estimators for Treatment Effects and Policy Evaluation
        PY  - 2015/Jan/
        PB  - Institute of Labor Economics (IZA)
        CY  - Bonn
        T2  - IZA Discussion Paper
        IS  - 8756
        UR  - https://www.iza.org/index.php/publications/dp8756
        AB  - This paper investigates the finite sample performance of a comprehensive set of semi- and nonparametric estimators for treatment and policy evaluation. In contrast to previous simulation studies which mostly considered semiparametric approaches relying on parametric propensity score estimation, we also consider more flexible approaches based on semi- or nonparametric propensity scores, nonparametric regression, and direct covariate matching. In addition to (pair, radius, and kernel) matching, inverse probability weighting, regression, and doubly robust estimation, our studies also cover recently proposed estimators such as genetic matching, entropy balancing, and empirical likelihood estimation.
We vary a range of features (sample size, selection into treatment, effect heterogeneity, and correct/misspecification) in our simulations and find that several nonparametric estimators by and large outperform commonly used treatment estimators using a parametric propensity score. Nonparametric regression, nonparametric doubly robust estimation, nonparametric IPW, and one-to-many covariate matching perform best.
        KW  - treatment effects
KW  - policy evaluation
KW  - simulation
KW  - empirical Monte Carlo study
KW  - propensity score
KW  - semi- and nonparametric estimation
        ER  -