Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements

Annals of Applied Statistics - Tập 12 Số 1 - 2018
Christian Fong1,2,3,4,5, Chad Hazlett1,2,3,4,5, Kosuke Imai1,2,3,4,5
1DEPARTMENT OF POLITICS AND CENTER FOR STATISTICS AND MACHINE LEARNING PRINCETON UNIVERSITY PRINCETON, NEW JERSEY 08544 USA
2DEPARTMENTS OF STATISTICS AND POLITICAL SCIENCE UNIVERSITY OF CALIFORNIA, LOS ANGELES LOS ANGELES, CALIFORNIA 90095 USA
3Graduate School of Business, Stanford University, Stanford, California 94305 USA
4Princeton University
5Stanford University, University of California, Los Angeles and

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Stuart, E. A. (2010). Matching methods for causal inference: A review and a look forward. <i>Statist. Sci.</i> <b>25</b> 1–21.

Imai, K. and van Dyk, D. A. (2004). Causal inference with general treatment regimes: Generalizing the propensity score. <i>J. Amer. Statist. Assoc.</i> <b>99</b> 854–866.

Imbens, G. W. (2000). The role of the propensity score in estimating dose-response functions. <i>Biometrika</i> <b>87</b> 706–710.

Rosenbaum, P. R. and Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. <i>Biometrika</i> <b>70</b> 41–55.

Rosenbaum, P. R. and Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. <i>Amer. Statist.</i> <b>39</b> 33–38.

Zubizarreta, J. R. (2015). Stable weights that balance covariates for estimation with incomplete outcome data. <i>J. Amer. Statist. Assoc.</i> <b>110</b> 910–922.

Abadie, A. and Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. <i>Econometrica</i> <b>74</b> 235–267.

Boyd, C. L., Epstein, L. and Martin, A. D. (2010). Untangling the causal effects of sex on judging. <i>Amer. J. Polit. Sci.</i> <b>54</b> 389–411.

Chan, K. C. G., Yam, S. C. P. and Zhang, Z. (2016). Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting. <i>J. R. Stat. Soc. Ser. B. Stat. Methodol.</i> <b>78</b> 673–700.

De, P. K. and Ratha, D. (2012). Impact of remittances on household income, asset, and human capital: Evidence from Sri Lanka. <i>Migr. Dev.</i> <b>1</b> 163–179.

Donohue III, J. J. and Ho, D. E. (2007). The impact of damage caps on malpractice claims: Randomization inferences with difference-in-differences. <i>J. Empir. Leg. Stud.</i> <b>4</b> 69–102.

Fong, C., Hazlett, C. and Imai, K. (2018). Replication data for: Covariate balancing propensity score for a continuous treatment. <a href="DOI:10.7910/DVN/AIF4PI">DOI:10.7910/DVN/AIF4PI</a>.

Fong, C., Ratkovic, M., Hazlett, C. and Imai, K. (2017). CBPS: R package for covariate balancing propensity score. Available at the Comprehensive R Archive Network (CRAN): <a href="https://CRAN.R-project.org/package=CBPS">https://CRAN.R-project.org/package=CBPS</a>.

Graham, B. S., Pinto, C. and Egel, D. (2012). Inverse probability tilting for moment condition models with missing data. <i>Rev. Econ. Stud.</i> <b>79</b> 1053–1079.

Hainmueller, J. (2012). Entropy balancing for causal effects: Multivariate reweighting method to produce balanced samples in observational studies. <i>Polit. Anal.</i> <b>20</b> 25–46.

Harder, V. S., Stuart, E. A. and Anthony, J. C. (2008). Adolescent cannabis problems and young adult depression: Male–female stratified propensity score analyses. <i>Am. J. Epidemiol.</i> <b>168</b> 592–601.

Hirano, K., Imbens, G. W. and Ridder, G. (2003). Efficient estimation of average treatment effects using the estimated propensity score. <i>Econometrica</i> <b>71</b> 1161–1189.

Ho, D. E., Imai, K., King, G. and Stuart, E. A. (2007). Matching as nonparametric preprocessing for reducing model dependence in parametric causal inference. <i>Polit. Anal.</i> <b>15</b> 199–236.

Imai, K. and Ratkovic, M. (2014). Covariate balancing propensity score. <i>J. R. Stat. Soc. Ser. B. Stat. Methodol.</i> <b>76</b> 243–263.

Imai, K. and Ratkovic, M. (2015). Robust estimation of inverse probability weights for marginal structural models. <i>J. Amer. Statist. Assoc.</i> <b>110</b> 1013–1023.

Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. <i>Rev. Econ. Stat.</i> <b>86</b> 4–29.

Kang, J. D. Y. and Schafer, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. <i>Statist. Sci.</i> <b>22</b> 523–539.

McCaffrey, D. F., Ridgeway, G. and Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. <i>Psychol. Methods</i> <b>9</b> 403–425.

Newey, W. K. and McFadden, D. (1994). Large sample estimation and hypothesis testing. In <i>Handbook of Econometrics</i>, <i>Vol. IV. Handbooks in Econom.</i> <b>2</b> 2111–2245. North-Holland, Amsterdam.

Nielsen, R. A., Findley, M. G., Davis, Z. S., Candland, T. and Nielson, D. L. (2011). Foreign aid shocks as a cause of violent armed conflict? <i>Amer. J. Polit. Sci.</i> <b>55</b> 219–232.

Robins, J. M., Hernán, M. Á. and Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. <i>Epidemiology</i> <b>11</b> 550–560.

Rosenbaum, P. R. and Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. <i>J. Amer. Statist. Assoc.</i> <b>79</b> 516–524.

Rubin, D. B. (1990). Comments on “On the application of probability theory to agricultural experiments. Essay on principles. Section 9” by J. Splawa-Neyman translated from the Polish and edited by D. M. Dabrowska and T. P. Speed. <i>Statist. Sci.</i> <b>5</b> 472–480.

Smith, J. A. and Todd, P. E. (2005). Does matching overcome LaLonde’s critique of nonexperimental estimators? <i>J. Econometrics</i> <b>125</b> 305–353.

Tan, Z. (2010). Bounded, efficient and doubly robust estimation with inverse weighting. <i>Biometrika</i> <b>97</b> 661–682.

Urban, C. and Niebler, S. (2014). Dollars on the sidewalk: Should US presidential candidates advertise in uncontested states? <i>Amer. J. Polit. Sci.</i> <b>58</b> 322–336.

Zhao, Q. and Percival, D. (2017). Entropy balancing is doubly robust. <i>J. Causal Inference</i> <b>5</b> 20160010.

Zhu, Y., Coffman, D. L. and Ghosh, D. (2015). A boosting algorithm for estimating generalized propensity scores with continuous treatments. <i>J. Causal Inference</i> <b>3</b> 25–40.

Fan, J., Imai, K., Liu, H., Ning, Y. and Yang, X. (2016). Improving covariate balancing propensity score: A doubly robust and efficient approach. Technical report, Princeton Univ.

Hazlett, C. (2016). Kernel balancing: A flexible non-parametric weighting procedure for estimating causal effects. Technical report, Depts. Statistics and Political Science, Univ. California Los Angeles.

Hirano, K. and Imbens, G. W. (2004). The propensity score with continuous treatments. In <i>Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives</i>: <i>An Essential Journey with Donald Rubin’s Statistical Family</i> 73–84. Wiley, New York.

Owen, A. B. (2001). <i>Empirical Likelihood</i>. Chapman &amp; Hall/CRC, Boca Raton, FL.

Zhao, Q. (2016). Covariate balancing propensity score by tailored loss functions. Technical report. Dept. Statistics, Stanford Univ.