New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators∗

نویسندگان

  • Matias Busso
  • John DiNardo
  • Justin McCrary
  • Alberto Abadie
  • Matias Cattaneo
  • Keisuke Hirano
چکیده

Frölich (2004) compares the finite sample properties of reweighting and matching estimators of average treatment effects and concludes that reweighting performs far worse than even the simplest matching estimator. We argue that this conclusion is unjustified. Neither approach dominates the other uniformly across data generating processes (DGPs). Expanding on the Frölich’s (2004) analysis, this paper analyzes empirical as well as hypothetical DGPs and examines misspecification. We conclude that reweighting is competitive with the most effective matching estimators when overlap is good, but that matching may be more effective when overlap is sufficiently poor. JEL Classification: C21

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators

Currently available asymptotic results in the literature suggest that matching estimators have higher variance than reweighting estimators. The extant literature comparing the finite sample properties of matching to specific reweighting estimators, however, has concluded that reweighting performs far worse than even the simplest matching estimator. We resolve these puzzling conclusions. Specifi...

متن کامل

Finite Sample Properties of Semiparametric Estimators of Average Treatment Effects∗

We explore the finite sample properties of several semiparametric estimators of average treatment effects, including propensity score reweighting, matching, double robust, and control function estimators. When there is good overlap in the distribution of propensity scores for treatment and control units, reweighting estimators are preferred on bias grounds and attain the semiparametric efficien...

متن کامل

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

The Finite Sample Performance of Semiand Nonparametric Estimators for Treatment Effects and Policy Evaluation This paper investigates the finite sample performance of a comprehensive set of semiand nonparametric estimators for treatment and policy evaluation. In contrast to previous simulation studies which mostly considered semiparametric approaches relying on parametric propensity score estim...

متن کامل

Comparing continuous treatment matching methods in policy evaluation

The paper evaluates the statistical properties of two different matching estimators in the case of continuous treatment, using a Montecarlo experiment. The traditional generalized propensity score matching estimator is compared whit a new two steps matching estimator for the continuous treatment case, recently developed [1]. It compares treatment and control units similar in terms of their obse...

متن کامل

استفاده از Propensity Score برای همسان سازی نمونه ها در یک مطالعه مورد شاهدی

Background and Aim: Case-Control studies provide evidence in the area of health. Validity and accuracy of such studies depend to a large extent on the similarity (similar distributions) of the case and control groups according to confounding variables. Matching is a method for controlling or eliminating the effects of important confounders. Matching using propensity score has recently been intr...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013