Paweł Strawiński
ARTICLE

(English) PDF

ABSTRACT

A caliper mechanism is a common tool used to prevent from inexact matches. The existing literature discusses asymptotic properties of matching with caliper. In this simulation study we investigate properties in small and medium sized samples. We show that caliper causes a significant bias of the ATT estimator and raises its variance in comparison to one-to-one matching.

KEYWORDS

propensity score matching, caliper, Monte Carlo experiment, finite sample properties

REFERENCES

[1] Abadie A., Imbens G., (2006), Large Sample Properties of Matching Estimators for Average Treatment Effects, Econometrica, 74 (1), 235–267.

[2] Abadie A. Imbens G., (2011), Bias-Corrected Estimates for Average Treatment Effects, Journal of Business and Economic Statistics, 29, 1–11.

[3] Austin P. (2009), Some Methods of Propensity Score Matching Had Superior Performance to Others: Result of an Empirical Investigation and Monte Carlo Simulations, Biometrical Journal, 5, 171–184.

[4] Blundell R., Costa-Diás M., (2000), Evaluation Methods for Non-Experimental Data, Fiscal Studies, 21 (4), 427–468.

[5] Busso M., DiNardo J., McCrary J., (2009), New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators, IZA Discussion Paper, 3998.

[6] Cochrane W., Rubin D., (1973), Controling Bias in Observational Studies. A Review, Sankhya, 35, 417–466.

[7] Dehejia R., Wahba S., (1999), Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Program, Journal of American Statistical Association, 94, 1053–1062.

[8] Dehejia R., Wahba S., (2002), Propensity Score Matching Methods for Nonexperimental Causal Studies, Journal of the American Statistical Association, 84, 151–161.

[9] Frölich (2004), Finite Sample Properties of Propensity-Score Matching and Weighting Estimators, The Review of Economics and Statistics, 86 (1), 77–90.

[10] Heckman J., Ichimura H., Smith J., Todd P., (1998), Characterizing Selection Bias Using Experimental Data, Econometrica, 66 (5), 1017–1098.

[11] Heckman J., Ichimura H., Todd P., (1997), Matching as an Econometric Evaluation Estimator: Evidence from Evaluating a Job Training Programme, The Review of Economic Studies, 64 (4), 605–654.

[12] Hirano K., Imbens G., Ridder G., (2003), Effi cient Estimation of Average Treatment Effects Using the Estimated Propensity Score, Econometrica, 71 (4), 1161–1189.

[13] Huber D., Lechner M., Wunch C., (2013), The Performance of Estimators Based on the Propensity Score, Journal of Econometrics, 175 (1), 1–21.

[14] Imbens G., (2004), Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review, Review of Economics and Statistics, 86 (1), 4–29.

[15] Lee M-J., (2005) Micro-Econometrics for Policy, Program, and Treatment Effects, Oxford University Press.

[16] Rosenbaum P., Rubin D., (1983), The Central Role of the Propensity Score in Observational Studies for Causal Effects, Biometrika, 70 (1), 41–55.

[17] Rosenbaum P., Rubin D., (1985a), Constructing Control Group Using Multivariate Matched Sampling Methods That Incorporate Propensity Score, The American Statistician, 39 (1), 33–38.

[18] Rosenbaum P., Rubin D., (1985b), Bias Due to Incomplete Matching, Biometrics, 41 (1), 103–116.

[19] Rubin D., (1973), Matching to Remove Bias in Observational Studies, Biometrics, 29, 159–183.

[20] Rubin D., (1980), Bias Reduction Using Mahalanobis Metric Matching, Biometrics, 36 (2), 293–298.

[21] Smith J., Todd P., (2001), Reconciling Confl icting Evidence on the Performance of Propensity-Score Matching Methods, The American Economic Review, 91 (2), 112–118.

[22] Smith J., Todd P., (2005), Does Matching Overcome LaLonde’s Critique of Nonexperimental Estimators?, Journal of Econometrics, 125, 305–353.

[23] Zhao Z., (2004), Using Matching to Estimate Treatment Effects: Data Requirements, Matching Methods, and Monte Carlo Evidence, The Review of Economics and Statistics, 86 (1), 91–107.

Back to top
© 2019–2022 Copyright by Statistics Poland, some rights reserved. Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0) Creative Commons — Attribution-ShareAlike 4.0 International — CC BY-SA 4.0