Using Matching to Estimate Treatment Effects: Data Requirements, Matching Metrics, and Monte Carlo Evidence

By Zhong Zhao. Review of Economics and Statistics, 2004

442 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/81d098e8518c519963d84f12c1463f9854cfc592

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Statistics and Probability

Citations in the corpus

Matching and semi-parametric IV estimation, a distance-based measure of migration, and the wages of young men (2011, background): “See Frölich (2004) and Zhao (2004) for Monte Carlo evaluations of various matching approaches.”
The performance of estimators based on the propensity score (2013, background): “…as it remains consistent if the misspecified propensity score model is a monotone transformation of the true model (see the simulation results in Drake, 1993, Zhao, 2004, 2008, Millimet and Tchernis, 2009, and Huber, 2011, suggesting some robustness to the specification of the propensity score).”
Propensity Scores (2005, unknown): “Some of this work is theoretical (Heckman and Navarro-Lozano 2004; Imbens 2004), some uses simulation data (Angrist and Hahn 2004; Frolich 2004; Zhao 2004), and some applies propensity scores to data from past quasi-experiments (Agodini and Dynarski 2004; Behrman, Cheng, and Todd 2004; Glazerman, Le”
The effects of public support schemes on small and medium enterprises (2015, background): “When the number of covariates is large propensity score matching is more suitable; the Mahalanobis distance works quite well when the number of covariates is fewer than eight, and when the sample is small (Zhao, 2004).”
The prognostic analogue of the propensity score (2008, methodology): “Miettinen (1976) proposes regression of Y on Z and X, followed by subclassification on the part of the linear predictor that is free of Z. Zhao (2004), in a suggestion echoed by Imbens (2004), proposes matching on a weighted combination of differences in covariates, using separate control- and…”
Evaluating the impact of education on earnings in the UK: models, methods and results from the NCDS (2003, background): “Simple parametric specifications for the propensity score have indeed often been found to be quite effective in achieving the required balancing (see e.g. Zhao (2004)).”
The Performance of Socially Responsible Mutual Funds: The Role of Fees and Management Companies (2010, unknown): “Finding control observations, however, is not easy when matching is done on several control variables, since exact or nearly exact matches for all variables and observations are rare even in large data sets (Zhao, 2004).”
The Effects of Derivatives on Firm Risk and Value (2011, unknown): “(See Zhao (2004) for more discussion on this point.”
Propensity score matching and variations on the balancing test (2011, unknown): “Despite similarities in the ideas between propensity score matching and covariate matching, the theory behind propensity score matching is quite different from that behind covariate matching (Zhao 2004).”
Evaluation of credit guarantee policy using propensity score matching (2009, background): “It should be clear that there is no winner for all situations and the choice of the estimator crucially depends on the situation at hand, especially data structure (Zhao, 2004).”
It Pays to Write Well (2017, unknown): “We do Mahalanobis-metric matching rather than propensityscore matching because the former has been found to be more robust to sampling bias (Zhao, 2004).”
Evaluating the effect of education on earnings: models, methods and results from the National Child Development Survey (2005, background): “(For a comparison of the small sample properties of matching estimators, see Abadie and Imbens (2002), Angrist and Han (2004), Frölich (2004) and Zhao (2004).)”