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Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements
Open accessAbstract
Propensity score matching and weighting are popular methods when estimating causal effects in observational studies. Beyond the assumption of unconfoundedness, however, these methods also require the model for the propensity score to be correctly specified. The recently proposed covariate balancing propensity score (CBPS) methodology increases the robustness to model misspecification by directly optimizing sample covariate balance between the treatment and control groups. In this paper, we extend the CBPS to a continuous treatment. We propose the covariate balancing generalized propensity score (CBGPS) methodology, which minimizes the association between covariates and the treatment. We develop both parametric and nonparametric approaches and show their superior performance over the standard maximum likelihood estimation in a simulation study. The CBGPS methodology is applied to an observational study, whose goal is to estimate the causal effects of political advertisements on campaign contributions. We also provide open-source software that implements the proposed methods.
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Citations
Cites
The central role of the propensity score in observational studies for causal effects
ROSENBAUM, P. R., & RUBIN, D. B. (1983) · Biometrika · doi:10.1093/biomet/70.1.41 · Cited by 31,241
Rosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,472
Hainmueller, J. (2012) · Political Analysis · doi:10.1093/pan/mpr025 · Cited by 6,026
Marginal Structural Models and Causal Inference in Epidemiology
Robins, J. M., Hernán, M. Á., & Brumback, B. (2000) · Epidemiology · doi:10.1097/00001648-200009000-00011 · Cited by 5,581
Matching Methods for Causal Inference: A Review and a Look Forward
Stuart, E. A. (2010) · Statistical Science · doi:10.1214/09-sts313 · Cited by 5,478
Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference
Ho, D. E., Imai, K., King, G., & Stuart, E. A. (2007) · Political Analysis · doi:10.1093/pan/mpl013 · Cited by 4,285
Self-citationDoes matching overcome lalonde’s critique of nonexperimental estimators?
A. Smith, J., & E. Todd, P. (2005) · Journal of Econometrics · doi:10.1016/j.jeconom.2004.04.011 · Cited by 3,176
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
Rosenbaum, P. R., & Rubin, D. B. (1984) · Journal of the American Statistical Association · doi:10.1080/01621459.1984.10478078 · Cited by 3,082
Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review
Imbens, G. W. (2004) · Review of Economics and Statistics · doi:10.1162/003465304323023651 · Cited by 2,994
Large sample properties of matching estimators for average treatment effects
Abadie, A., & Imbens, G. W. (2006) · Econometrica · doi:10.1111/j.1468-0262.2006.00655.x · Cited by 2,908
Cited by
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Electoral Effects of Biased Media: Russian Television in Ukraine
Peisakhin, L. V., & Rozenas, A. (2017) · American Journal of Political Science · doi:10.1111/ajps.12355 · Cited by 225
Banks’ exposure to interest rate risk and the transmission of monetary policy
Gomez, M., Landier, A., Sraer, D. A., & Thesmar, D. (2020) · Journal of Monetary Economics · doi:10.1016/j.jmoneco.2020.03.011 · Cited by 158
Can Consumer-Posted Photos Serve as a Leading Indicator of Restaurant Survival? Evidence from Yelp
Zhang, M., & Luo, L. (2016) · Management Science · doi:10.1287/mnsc.2022.4359 · Cited by 169
Stable Prediction with Model Misspecification and Agnostic Distribution Shift
Kuang, K., Xiong, R., Cui, P., Athey, S. C., & Li, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5876 · Cited by 129
Entropy Balancing for Continuous Treatments
Tübbicke, S. (2021) · Journal of Econometric Methods · doi:10.1515/jem-2021-0002 · Cited by 120
Recent Developments in Causal Inference and Machine Learning
Brand, J. E., Zhou, X., & Xie, Y. F. (2023) · Annual Review of Sociology · doi:10.1146/annurev-soc-030420-015345 · Cited by 132
Using propensity scores to estimate effects of treatment initiation decisions: State of the science
Webster‐Clark, M., Stürmer, T., Wang, T., Man, K. K. C., Marinac‐Dabic, D., Rothman, K. J., Ellis, A. R., Gokhale, M., Lunt, M., Girman, C. J., et al. (2020) · Statistics in Medicine · doi:10.1002/sim.8866 · Cited by 101
WeightIt: Weighting for Covariate Balance in Observational Studies
Greifer, N. (2017) · doi:10.32614/cran.package.weightit · Cited by 92
Song, H., de Zúñiga, H. G., & Boomgaarden, H. G. (2019) · Mass Communication & Society · doi:10.1080/15205436.2019.1651867 · Cited by 89
Calic, G., & Shevchenko, A. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.04.060 · Cited by 86
Angling for Influence: Institutional Proliferation in Development Banking
Pratt, T. (2020) · International Studies Quarterly · doi:10.1093/isq/sqaa085 · Cited by 87
Austin, P. C. (2018) · Statistics in Medicine · doi:10.1002/sim.7615 · Cited by 80
Repression Works (Just Not in Moderation)
Zhukov, Y. M. (2023) · Comparative Political Studies · doi:10.1177/00104140231152778 · Cited by 81
Diversity, Institutions, and Economic Outcomes: Post-WWII Displacement in Poland
Charnysh, V. (2019) · American Political Science Review · doi:10.1017/s0003055419000042 · Cited by 76
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Siegel, K., & Dee, L. E. (2025) · Ecology Letters · doi:10.1111/ele.70053 · Cited by 85
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Zhang, Y., Xu, F., Xia, T., & Li, Y. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3494990 · Cited by 68
Vegetabile, B., Griffin, B. A., Coffman, D. L., Cefalu, M. S., Robbins, M. W., & McCaffrey, D. F. (2021) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-020-00236-2 · Cited by 62
Matching on Generalized Propensity Scores with Continuous Exposures
Wu, X., Mealli, F., Kioumourtzoglou, M., Dominici, F., & Braun, D. (2022) · Journal of the American Statistical Association · doi:10.1080/01621459.2022.2144737 · Cited by 87
Robust estimation of causal effects via a high-dimensional covariate balancing propensity score
Ning, Y., Sida, P., & Imai, K. (2020) · Biometrika · doi:10.1093/biomet/asaa020 · Cited by 80
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Narita, K., Tena, J. D. D., & Detotto, C. (2023) · The Leadership Quarterly · doi:10.1016/j.leaqua.2023.101678 · Cited by 54
Yiu, S., & Su, L. (2018) · Biometrika · doi:10.1093/biomet/asy015 · Cited by 46
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Luong, T. T., Sivarajah, U., & Weerakkody, V. (2021) · Information Systems Frontiers · doi:10.1007/s10796-019-09962-6 · Cited by 45
Ma, T. M., Sun, Y., Malone, S. C., Roach, M., Dearnaley, D. P., Pisansky, T. M., Feng, F. Y.-C., Sandler, H. M., Efstathiou, J. A., Syndikus, I., et al. (2022) · Journal of Clinical Oncology · doi:10.1200/jco.22.00970 · Cited by 44
Zhao, S., van Dyk, D. A., & Imai, K. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280219888745 · Cited by 44
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Wu, K., Guo, B., Guo, Y., Han, M., Xu, H. Y., Luo, R., Hong, Z., Zhang, B., Dong, K., Wu, J., et al. (2022) · Environment International · doi:10.1016/j.envint.2022.107216 · Cited by 45
Barlow, P. L., Loopstra, R., Tarasuk, V. S., & Reeves, A. (2020) · The Lancet Global Health · doi:10.1016/s2214-109x(20)30263-1 · Cited by 43
Propensity score analysis methods with balancing constraints: A Monte Carlo study
Li, Y., & Li, L. (2021) · Statistical Methods in Medical Research · doi:10.1177/0962280220983512 · Cited by 42
Kroeger, C. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01684-9 · Cited by 39
Gist, K. M., Menon, S., Antón-Martín, P., Bigelow, A. M., Cortina, G., Deep, A., Navazo, S. D. L. M., Gelbart, B., Gorga, S. M., Guzzo, I., et al. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.49871 · Cited by 44
Somvanshi, P. R., Mellon, S. H., Flory, J. D., Abu-Amara, D., PTSD Systems Biology Consortium;, Wolkowitz, O. M., Yehuda, R., Jett, M., Hood, L., Marmar, C., et al. (2019) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00065.2019 · Cited by 43
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Hübner, N., Wagner, W., Zitzmann, S., & Nagengast, B. (2023) · Educational Psychology Review · doi:10.1007/s10648-023-09724-6 · Cited by 50
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Chatton, A., & Rohrer, J. M. (2024) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459241236149 · Cited by 39
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Hazlett, C. J. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2746753 · Cited by 36
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Silva, P. C. (2021) · Journal of Elections Public Opinion and Parties · doi:10.1080/17457289.2021.1952210 · Cited by 31
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Rawal, A., Raglin, A. J., Rawat, D. B., Sadler, B. M., & McCoy, J. (2024) · ACM Computing Surveys · doi:10.1145/3665494 · Cited by 38
Merla, L., Montesi, M. C., Ticali, J., Bais, B., Cavarape, A., & Colussi, G. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12051845 · Cited by 30
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Fan, J., Imai, K., Lee, I., Liu, H., Ning, Y., & Yang, X. (2021) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2021.2002159 · Cited by 27
Self-citationPropensity score stratification methods for continuous treatments
Brown, D. W., Greene, T. J., Swartz, M. D., Wilkinson, A. V., & DeSantis, S. M. (2020) · Statistics in Medicine · doi:10.1002/sim.8835 · Cited by 25
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Ai, C., Linton, O., Motegi, K., & Zhang, Z. (2021) · Quantitative Economics · doi:10.3982/qe1494 · Cited by 23
Kıbrıs, A. (2021) · Journal of Peace Research · doi:10.1177/0022343320978695 · Cited by 26
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Menon, A. (2022) · Comparative Political Studies · doi:10.1177/00104140221141833 · Cited by 23
Tian, F., Wang, Y., Huang, Z., Qian, A. M., Wang, C., Tan, L., McMillin, S. E., Abresch, C., Zhang, Z., & Lin, H. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.136498 · Cited by 24
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Harnois‐Leblanc, S., Sylvestre, M., Hulst, A. V., Barnett, T. A., Mathieu, M.-E., Mésidor, M., McGrath, J. J., Tremblay, A., Drapeau, V., Paradis, G., et al. (2022) · The Lancet Child & Adolescent Health · doi:10.1016/s2352-4642(22)00278-4 · Cited by 26
Tiscornia, L. (2023) · Journal of Peace Research · doi:10.1177/00223433221128846 · Cited by 23
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Deakin, C. T., Stavola, B. L. D., Littlejohn, G. O., Griffiths, H., Ciciriello, S., Youssef, P. P., Mathers, D., Bird, P., Smith, T., O’Sullivan, C. P., et al. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.20851 · Cited by 16
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Metadata
- Authors
- Fong, C., Hazlett, C., & Imai, K.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- The Annals of Applied Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2018-03-01
- Volume
- 12
- Issue
- 1
- Pages
- 156-177
- ISSN
- 1932-6157 (ISSN-L)
- 1941-7330
- OpenAlex
- W2585690194
- Semantic Scholar
- 4bc994580cf65394d0b45ca675c00742018bea64
- Cited by
- 315
- References
- 39
- Citations by year
- 2026: 24 · 2025: 45 · 2024: 47 · 2023: 41 · 2022: 43 · 2021: 42 · 2020: 42 · 2019: 14 · 2018: 11
- FWCI
- 22.61
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Annals of Applied Statistics (publishedVersion)
- Project Euclid (Cornell University) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Keywords
- covariate balancing propensity score, continuous treatment, generalized propensity score, model misspecification, causal effect estimation, campaign contributions, observational studies, nonparametric estimation, parameter estimation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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