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The finite sample performance of inference methods for propensity score matching and weighting estimators

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    20.01.2016

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inference
variance estimation
treatment effects
matching
inverse probability weighting
English 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.
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Faculty
Faculté des sciences économiques et sociales et du management
Language
  • English
Classification
Economics
Series statement
  • Working Papers SES ; 466
License
License undefined
Identifiers
  • RERO DOC 258214
  • RERO R008348981
Persistent URL
https://folia.unifr.ch/unifr/documents/304723
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