Economics at your fingertips  

The finite sample performance of semi- and non-parametric estimators for treatment effects and policy evaluation

Markus Frölich, Martin Huber () and Manuel Wiesenfarth

Computational Statistics & Data Analysis, 2017, vol. 115, issue C, 91-102

Abstract: The finite sample performance of a comprehensive set of semi- and non-parametric estimators for treatment evaluation is investigated. The simulation design is based on Swiss labor market data and considers estimators based on parametric, semiparametric, and nonparametric propensity scores, as well as approaches directly controlling for covariates. Among the methods included are pair, radius, kernel, and genetic matching, inverse probability weighting, regression, doubly robust estimation, entropy balancing, and empirical likelihood estimation. The simulation designs vary w.r.t. sample size, selection into treatment, effect heterogeneity, and (non-)omission of a subset of the all in all 3 continuous and 11 binary confounders. Several nonparametric estimators outperform commonly used treatment estimators based on parametric propensity scores in terms of root mean squared error (RMSE), even though average RMSEs based on the 16 simulation designs considered are not statistically significantly different across the estimators investigated.

Keywords: Treatment effects; Policy evaluation; Simulation; Empirical Monte Carlo study; Propensity score; Semi- and non-parametric estimation (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only.

Related works:
Working Paper: The finite sample performance of semi- and nonparametric estimators for treatment effects and policy evaluation (2015) Downloads
Working Paper: The Finite Sample Performance of Semi- and Nonparametric Estimators for Treatment Effects and Policy Evaluation (2015) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Dana Niculescu ().

Page updated 2018-12-08
Handle: RePEc:eee:csdana:v:115:y:2017:i:c:p:91-102