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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
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 · see citing works
Rosenbaum, P., & Rubin, D. (1985) · The American Statistician · doi:10.2307/2683903 · Cited by 6,854 · see citing works
Hainmueller, J. (2012) · Political Analysis · doi:10.1093/pan/mpr025 · Cited by 6,026 · see citing works
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 · see citing works
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 · see citing works
Rosenbaum, P. R., & Rubin, D. B. (1985) · The American Statistician · doi:10.1080/00031305.1985.10479383 · Cited by 4,415 · see citing works
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 4,152 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
Hirano, K., Imbens, G. W., & Ridder, G. (2003) · Econometrica · doi:10.1111/1468-0262.00442 · Cited by 2,527 · see citing works
Large sample estimation and hypothesis testingPreprint
Newey, W. K., & McFadden, D. L. (1986) · RePEc: Research Papers in Economics · Cited by 2,192 · see citing works
The role of the propensity score in estimating dose-response functions
Imbens, G. W. (2000) · Biometrika · doi:10.1093/biomet/87.3.706 · Cited by 1,747 · see citing works
Chapter 36 Large sample estimation and hypothesis testing
Newey, W. K., & McFadden, D. (1994) · Handbook of Econometrics · doi:10.1016/s1573-4412(05)80005-4 · Cited by 1,743 · see citing works
On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
Neyman, J., Dabrowska, D. M., & Speed, T. P. (1990) · Statistical Science · doi:10.1214/ss/1177012031 · Cited by 1,616 · see citing works
Covariate Balancing Propensity Score
Imai, K., & Ratkovic, M. (2013) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12027 · Cited by 1,330 · see citing works
Self-citationMcCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004) · Psychological Methods · doi:10.1037/1082-989x.9.4.403 · Cited by 1,317 · see citing works
Kang, J. D. Y., & Schafer, J. L. (2007) · Statistical Science · doi:10.1214/07-sts227 · Cited by 1,259 · see citing works
The Propensity Score with Continuous Treatments
Hirano, K., & Imbens, G. W. (2004) · Wiley Series in Probability and Statistics · doi:10.1002/0470090456.ch7 · Cited by 1,064 · see citing works
Causal Inference With General Treatment Regimes
Imai, K., & van Dyk, D. V. (2004) · Journal of the American Statistical Association · doi:10.1198/016214504000001187 · Cited by 1,022 · see citing works
Self-citationUntangling the Causal Effects of Sex on Judging
Boyd, C. L., Epstein, L., & Martin, A. D. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2010.00437.x · Cited by 592 · see citing works
Entropy Balancing is Doubly Robust
Zhao, Q., & Percival, D. (2016) · Journal of Causal Inference · doi:10.1515/jci-2016-0010 · Cited by 402 · see citing works
Stable weights that balance covariates for estimation with incomplete outcome data
Zubizarreta, J. R. (2015) · Journal of the American Statistical Association · doi:10.1080/01621459.2015.1023805 · Cited by 369 · cited 2× · see citing works
“This contrasts with some other methods such as entropy balancing and stable weights that solve a convex optimization problem [Hainmueller (2012), Zubizarreta (2015)].”
BackgroundMethodKang, J., & Schafer, J. L. (2007) · Statistical Science · Cited by 368 · see citing works
Bounded, efficient and doubly robust estimation with inverse weighting
Tan, Z. (2010) · Biometrika · doi:10.1093/biomet/asq035 · Cited by 305 · see citing works
Foreign Aid Shocks as a Cause of Violent Armed Conflict
Nielsen, R. A., Findley, M. G., Davis, Z. S., Candland, T., & Nielson, D. (2011) · American Journal of Political Science · doi:10.1111/j.1540-5907.2010.00492.x · Cited by 233 · see citing works
Impact of remittances on household income, asset and human capital: evidence from Sri Lanka
Dea, P. K., & Rathab, D. (2012) · Migration and Development · doi:10.1080/21632324.2012.719348 · Cited by 183 · see citing works
Chan, K. C. G., Yam, S. C. P., & Zhang, Z. (2015) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/rssb.12129 · Cited by 175 · see citing works
Inverse probability tilting for moment condition models with missing data
Graham, B. S., De Xavier Pinto, C. C., & Egel, D. (2012) · The Review of Economic Studies · doi:10.1093/restud/rdr047 · Cited by 174 · cited 1× · see citing works
“In recent years, several researchers have proposed methods that estimate propensity scores by optimizing covariate balance (e.g., McCaffrey et al., 2004; Tan, 2010; Hainmueller, 2012; Graham et al., 2012; Imai and Ratkovic, 2014; Chan et al., 2015; Hazlett, 2016; Fan et al., 2016; Zhao, 2016).”
BackgroundCovariate balancing propensity score by tailored loss functions
Zhao, Q.-Y. (2016) · Annals of Statistics · doi:10.1214/18-aos1698 · Cited by 171 · see citing works
A Boosting Algorithm for Estimating Generalized Propensity Scores with Continuous Treatments
Zhu, Y., Coffman, D. L., & Ghosh, D. (2014) · Journal of Causal Inference · doi:10.1515/jci-2014-0022 · Cited by 155 · see citing works
Robust Estimation of Inverse Probability Weights for Marginal Structural Models
Imai, K., & Ratkovic, M. (2014) · Journal of the American Statistical Association · doi:10.1080/01621459.2014.956872 · Cited by 89 · see citing works
Self-citationHarder, V. S., Stuart, E. A., & Anthony, J. C. (2008) · American Journal of Epidemiology · doi:10.1093/aje/kwn184 · Cited by 76 · see citing works
Dollars on the Sidewalk: Should U.S. Presidential Candidates Advertise in Uncontested States?
Urban, C., & Niebler, S. E. (2014) · American Journal of Political Science · doi:10.1111/ajps.12073 · Cited by 38 · see citing works
Donohue, J. J., & Ho, D. E. (2007) · Journal of Empirical Legal Studies · doi:10.1111/j.1740-1461.2007.00082.x · Cited by 32 · see citing works
Kernel Balancing: A Flexible Non-Parametric Weighting Procedure for Estimating Causal EffectsPreprint
Hazlett, C. J. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2746753 · Cited by 31 · see citing works
Self-citationCovariate Balancing Propensity Score by Tailored Loss FunctionsPreprint
Zhao, Q. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1601.05890 · Cited by 18 · see citing works
Replication Data for: Covariate Balancing Propensity Score for a Continuous Treatment
Fong, C. H., Hazlett, C., & Imai, K. (2017) · Harvard Dataverse · doi:10.7910/dvn/aif4pi · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Yao, L.-Y., Chu, Z.-X., Li, S., Li, Y.-L., Gao, J., & Zhang, A.-D. (2020) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3444944 · Cited by 727 · see citing works
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 231 · see citing works
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 · see citing works
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 165 · see citing works
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 121 · see citing works
Entropy Balancing for Continuous Treatments
Tübbicke, S. (2021) · Journal of Econometric Methods · doi:10.1515/jem-2021-0002 · Cited by 120 · see citing works
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 124 · see citing works
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 94 · see citing works
WeightIt: Weighting for Covariate Balance in Observational Studies
Greifer, N. (2017) · doi:10.32614/cran.package.weightit · Cited by 92 · see citing works
Song, H., de Zúñiga, H. G., & Boomgaarden, H. G. (2019) · Mass Communication & Society · doi:10.1080/15205436.2019.1651867 · Cited by 89 · see citing works
Calic, G., & Shevchenko, A. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.04.060 · Cited by 86 · see citing works
Angling for Influence: Institutional Proliferation in Development Banking
Pratt, T. (2020) · International Studies Quarterly · doi:10.1093/isq/sqaa085 · Cited by 85 · see citing works
Austin, P. C. (2018) · Statistics in Medicine · doi:10.1002/sim.7615 · Cited by 80 · see citing works
Repression Works (Just Not in Moderation)
Zhukov, Y. (2023) · Comparative Political Studies · doi:10.1177/00104140231152778 · Cited by 25 · see citing works
Diversity, Institutions, and Economic Outcomes: Post-WWII Displacement in Poland
Charnysh, V. (2019) · American Political Science Review · doi:10.1017/s0003055419000042 · Cited by 76 · see citing works
Foundations and Future Directions for Causal Inference in Ecological Research
Siegel, K., & Dee, L. E. (2025) · Ecology Letters · doi:10.1111/ele.70053 · Cited by 73 · see citing works
Quantifying the Causal Effect of Individual Mobility on Health Status in Urban Space
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 · see citing works
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 · see citing works
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 56 · see citing works
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 69 · see citing works
Self-citationSomvanshi, P. R., Mellon, S. H., Yehuda, R., Flory, J. D., Makotkine, I., Bierer, L., Marmar, C., Jett, M., & Doyle, F. J. (2020) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00398.2019 · Cited by 55 · see citing works
Assessing multidimensional sustainability: Lessons from Brazil’s social protection programs
Dyngeland, C., Oldekop, J. A., & Evans, K. L. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1920998117 · Cited by 55 · see citing works
A survey on causal inference for recommendation
Luo, H., Zhuang, F., Xie, R., Zhu, H., Wang, D., An, Z., & Xu, Y. (2024) · The Innovation · doi:10.1016/j.xinn.2024.100590 · Cited by 59 · see citing works
Causal inference with observational data: A tutorial on propensity score analysis
Narita, K., Tena, J., & Detotto, C. (2023) · Leadership Quarterly · doi:10.1016/j.leaqua.2023.101678 · Cited by 47 · see citing works
Yiu, S., & Su, L. (2018) · Biometrika · doi:10.1093/biomet/asy015 · Cited by 46 · see citing works
Deforestation reduces fruit and vegetable consumption in rural Tanzania
Hall, C., Rasmussen, L. V., Powell, B., Dyngeland, C., Jung, S., & Olesen, R. S. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2112063119 · Cited by 45 · see citing works
Do Agile Managed Information Systems Projects Fail Due to a Lack of Emotional Intelligence?
Luong, T. T., Sivarajah, U., & Weerakkody, V. (2021) · Information Systems Frontiers · doi:10.1007/s10796-019-09962-6 · Cited by 45 · see citing works
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 · see citing works
Zhao, S., van Dyk, D. A., & Imai, K. (2020) · Statistical Methods in Medical Research · doi:10.1177/0962280219888745 · Cited by 44 · see citing works
Self-citationAssociation between residential greenness and gut microbiota in Chinese adults
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 44 · see citing works
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 · see citing works
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 41 · see citing works
Kroeger, C. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01684-9 · Cited by 39 · see citing works
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 42 · see citing works
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 40 · see citing works
“Since our data come from an observational study, we used covariate balancing propensity scores (CBPS) for weighting in estimation of average causal effects (16) and performed sensitivity analysis to determine the extent of violation of the ignorability assumption (20).”
Financial statement comparability and accounting fraud
Blanco, B., Dhole, S., & Gul, F. A. (2022) · Journal of Business Finance & Accounting · doi:10.1111/jbfa.12652 · Cited by 39 · see citing works
Hübner, N., Wagner, W., Zitzmann, S., & Nagengast, B. (2023) · Educational Psychology Review · doi:10.1007/s10648-023-09724-6 · Cited by 45 · see citing works
Diversification and resilience of firms in the agrifood supply chain
Stevens, A. W., & Teal, J. (2023) · American Journal of Agricultural Economics · doi:10.1111/ajae.12398 · Cited by 36 · see citing works
Stable inverse probability weighting estimation for longitudinal studies
Avagyan, V., & Vansteelandt, S. (2021) · Scandinavian Journal of Statistics · doi:10.1111/sjos.12542 · Cited by 36 · see citing works
Human Capital in Court: The Role of Attorney Experience in US Supreme Court Litigation
Nelson, M. J., & Epstein, L. (2021) · Journal of Law and Courts · doi:10.1086/714577 · Cited by 35 · see citing works
Independence Weights for Causal Inference with Continuous Treatments
Huling, J. D., Greifer, N., & Chen, G. (2023) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2213485 · Cited by 36 · see citing works
The Causal Cookbook: Recipes for Propensity Scores, G-Computation, and Doubly Robust Standardization
Chatton, A., & Rohrer, J. M. (2024) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459241236149 · Cited by 33 · see citing works
Kernel Balancing: A Flexible Non-Parametric Weighting Procedure for Estimating Causal EffectsPreprint
Hazlett, C. J. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2746753 · Cited by 31 · see citing works
Self-citationDistrict magnitude, electoral coordination, and legislative fragmentation
Silva, P. C. (2021) · Journal of Elections Public Opinion and Parties · doi:10.1080/17457289.2021.1952210 · Cited by 30 · see citing works
Causality for Trustworthy Artificial Intelligence: Status, Challenges and Perspectives
Rawal, A., Raglin, A. J., Rawat, D. B., Sadler, B. M., & McCoy, J. (2024) · ACM Computing Surveys · doi:10.1145/3665494 · Cited by 35 · see citing works
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 · see citing works
Optimal Covariate Balancing Conditions in Propensity Score Estimation
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 · see citing works
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 · see citing works
A unified framework for efficient estimation of general treatment models
Ai, C., Linton, O., Motegi, K., & Zhang, Z. (2021) · Quantitative Economics · doi:10.3982/qe1494 · Cited by 23 · see citing works
Kıbrıs, A. (2021) · Journal of Peace Research · doi:10.1177/0022343320978695 · Cited by 26 · see citing works
The Political Legacy of Forced Migration: Evidence from Post-WWII Germany
Menon, A. (2022) · Comparative Political Studies · doi:10.1177/00104140221141833 · Cited by 23 · see citing works
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 23 · see citing works
Reading to learn? The co-development of mathematics and reading during primary school
Hübner, N., Merrell, C. H., Cramman, H., Little, J. B., Bolden, D. S., & Nagengast, B. (2022) · Child Development · doi:10.1111/cdev.13817 · Cited by 23 · see citing works
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 23 · see citing works
Tiscornia, L. (2023) · Journal of Peace Research · doi:10.1177/00223433221128846 · Cited by 23 · see citing works
Davis, C. L., & Morse, J. C. (2018) · International Studies Quarterly · doi:10.1093/isq/sqy022 · Cited by 22 · see citing works
Residual Balancing: A Method of Constructing Weights for Marginal Structural Models
Zhou, X., & Wodtke, G. T. (2020) · Political Analysis · doi:10.1017/pan.2020.2 · Cited by 21 · see citing works
Counterfactual Prediction for Bundle Treatment
Zou, H., Cui, P., Li, B., Shen, Z., Ma, J., Yang, H., & He, Y. (2020) · neural information processing systems · Cited by 22 · see citing works
CE-RCFR: Robust Counterfactual Regression for Consensus-Enabled Treatment Effect Estimation
Wang, F., Chen, C., Liu, W., Fan, T. Y., Liao, X., Tan, Y., Qi, L., & Zheng, X. (2024) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3637528.3672054 · Cited by 21 · see citing works
Shi, C., Hu, P., Fan, W., & Qiu, L. (2023) · Internet Research · doi:10.1108/intr-07-2022-0510 · Cited by 21 · see citing works
Continual learning in medical image analysis: A survey
Wu, X., Xu, Z., & Tong, R. K. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109206 · Cited by 22 · see citing works
Brazilian Land Registry Impacts on Land Use Conversion
Jung, S., Dyngeland, C., Rausch, L. L., & Rasmussen, L. V. (2021) · American Journal of Agricultural Economics · doi:10.1111/ajae.12217 · Cited by 20 · see citing works
CBPS: Covariate Balancing Propensity Score
Fong, C., Ratkovic, M., & Imai, K. (2012) · doi:10.32614/cran.package.cbps · Cited by 20 · see citing works
Self-citationAkhlaghi, N., Needham, D. M., Bose, S., Banner‐Goodspeed, V., Beesley, S. J., Dinglas, V. D., Groat, D., Greene, T., Hopkins, R. O., Jackson, J. C., et al. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-040830 · Cited by 19 · see citing works
Zhao, Y., & Li, M. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17218171 · Cited by 19 · see citing works
Can User Generated Content Predict Restaurant Survival: Deep Learning of Yelp Photos and ReviewsPreprint
Zhang, M., & Luo, L. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.3108288 · Cited by 19 · see citing works
Zhu, H., Lu, D., Branch, D. W., Troendle, J. F., Tang, Y., Bernitz, S., Zamora, J., Betrán, A. P., Zhou, Y., & Zhang, J. (2023) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2023.07.054 · Cited by 18 · see citing works
Hobeika, C., Tribillon, E., Marchèse, U., Faermark, N., Ghedira, A., Bonnet, S., Nassar, A., Gayet, B., & Fuks, D. (2021) · Surgery · doi:10.1016/j.surg.2021.05.034 · Cited by 17 · see citing works
CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution
Yao, D., Gong, C., Zhang, L., Chen, S., & Bi, J. (2022) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3534678.3539108 · Cited by 17 · see citing works
Electoral impacts of a failed uprising: Evidence from Hong Kong's Umbrella Movement
Wang, Y., & Wong, S. H. (2021) · Electoral Studies · doi:10.1016/j.electstud.2021.102336 · Cited by 17 · see citing works
Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments
Colangelo, K., & Lee, Y. (2025) · Journal of Business and Economic Statistics · doi:10.1080/07350015.2025.2505487 · Cited by 16 · see citing works
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 · see citing works
Optimal balancing of time-dependent confounders for marginal structural models
Kallus, N., & Santacatterina, M. (2021) · Journal of Causal Inference · doi:10.1515/jci-2020-0033 · Cited by 18 · see citing works
Overhead aversion and facial expressions in crowdfunding
Yoo, J. J., Song, S., & Jhang, J. (2022) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2022.103101 · Cited by 16 · see citing works
Matching on Generalized Propensity Scores with Continuous ExposuresPreprint
Wu, X., Mealli, F., Kioumourtzoglou, M., Dominici, F., & Braun, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.06575 · Cited by 16 · see citing works
VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous TreatmentsPreprint
Nie, L., Ye, M., Liu, Q., & Nicolae, D. L. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.07861 · Cited by 16 · see citing works
Song, Z., & Wu, Y. (2022) · Habitat International · doi:10.1016/j.habitatint.2022.102663 · Cited by 15 · see citing works
Keller, B., & Branson, Z. (2024) · Asia Pacific Education Review · doi:10.1007/s12564-024-09957-2 · Cited by 18 · see citing works
Barlow, P. L. (2020) · Health & Place · doi:10.1016/j.healthplace.2020.102329 · Cited by 15 · see citing works
Kindergarten Obesity and Academic Achievement: The Mediating Role of Weight Bias
Yu, B. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.640474 · Cited by 16 · see citing works
Yue, L. Q., & Wang, J. (2023) · Business & Society · doi:10.1177/00076503231182666 · Cited by 14 · see citing works
Zhang, Y., Chen, S., Wei, J., Jiang, J., Lin, X., Wang, Y., Hao, C., Wu, W., Yuan, Z., Sun, J., et al. (2024) · 中国科学通报:英文版 · doi:10.1016/j.scib.2024.03.028 · Cited by 14 · see citing works
Brown, D. W., DeSantis, S., Greene, T. J., Maroufy, V., Yaseen, A., Wu, H.-L., Williams, G., & Swartz, M. (2020) · Statistics in Medicine · doi:10.1002/sim.8540 · Cited by 15 · see citing works
Could rainfall have swung the result of the Brexit referendum?
Leslie, P., & Arı, B. (2018) · Political Geography · doi:10.1016/j.polgeo.2018.05.009 · Cited by 14 · see citing works
Shang, X., Klaiber, H. A., & Miteva, D. A. (2022) · American Journal of Agricultural Economics · doi:10.1111/ajae.12344 · Cited by 13 · see citing works
Filing speed, information leakage, and price formation
Callen, J. L., Kaniel, R., & Segal, D. (2022) · Review of Accounting Studies · doi:10.1007/s11142-022-09673-5 · Cited by 13 · see citing works
Connected in health: Place-to-place commuting networks and COVID-19 spillovers
Seto, C. H.-F., Graif, C., Khademi, A., Honavar, V., & Kelling, C. (2022) · Health & Place · doi:10.1016/j.healthplace.2022.102891 · Cited by 13 · see citing works
Force Structure and Local Peacekeeping Effectiveness: Micro-Level Evidence on UN Troop Composition
Dworschak, C., & Cil, D. (2022) · International Studies Quarterly · doi:10.1093/isq/sqac072 · Cited by 15 · see citing works
Bach‐Mortensen, A., Goodair, B., & Barlow, J. (2023) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2023.106245 · Cited by 13 · see citing works
Improving Effect Estimates by Limiting the Variability in Inverse Propensity Score Weights
Kranker, K., Blue, L., & Forrow, L. V. (2020) · The American Statistician · doi:10.1080/00031305.2020.1737229 · Cited by 13 · see citing works
How Voters Respond to Currency Crises: Evidence From Turkey
Steinberg, D. A. (2021) · Comparative Political Studies · doi:10.1177/00104140211060268 · Cited by 13 · see citing works
Can the constitutional concentration of power be linked to Africa's energy poverty?
Ndoya, H., Kamguia, B., & Nchofoung, T. N. (2024) · Energy Policy · doi:10.1016/j.enpol.2024.114312 · Cited by 12 · see citing works
Terrorism, banking, and informal savings: Evidence from Nigeria
Posso, A. (2023) · Journal of Banking & Finance · doi:10.1016/j.jbankfin.2023.106822 · Cited by 12 · see citing works
Lonely schools: the relationship between geographic isolation and academic attainment
Odell, E. (2017) · Educational Research · doi:10.1080/00131881.2017.1339285 · Cited by 12 · see citing works
Huang, D. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3641282 · Cited by 11 · see citing works
The Racially Disparate Influence of Filing Fees on Eviction Rates
Gomory, H., Massey, D. S., Hendrickson, J. R., & Desmond, M. (2023) · Housing Policy Debate · doi:10.1080/10511482.2023.2212662 · Cited by 11 · see citing works
Tutorial on Matching-based Causal Analysis of Human Behaviors Using Smartphone Sensor Data
Jung, G., Park, S., Ma, E.-Y., Kim, H., & Lee, U. (2024) · ACM Computing Surveys · doi:10.1145/3648356 · Cited by 9 · see citing works
Area-Based Hedonic Pricing of Urban Green Amenities in Beijing: A Spatial Piecewise Approach
Liu, Z., Huang, H., Siikamaki, J. V., & Xu, J. (2024) · Environmental and Resource Economics · doi:10.1007/s10640-024-00861-2 · Cited by 11 · see citing works
Neuroticism and the sales profession
Habel, J., Kadić‐Maglajlić, S., Hartmann, N. N., de Jong, A., Zacharias, N. A., & Kosse, F. (2024) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2024.104353 · Cited by 11 · see citing works
Estimating causal effects with optimization-based methods: A review and empirical comparison
Cousineau, M., Verter, V., Murphy, S. A., & Pineau, J. (2022) · European Journal of Operational Research · doi:10.1016/j.ejor.2022.01.046 · Cited by 11 · see citing works
- 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 · 2017: 4 · 2016: 2
- 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 ›
Are you an author, or do you see a problem with this page? Request a review