The DOI matches its Crossref record; 38 of the 39 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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.
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
Sorted by citations
Yao, L., Chu, Z., Li, S., Li, Y., Gao, J., & Zhang, A. (2021) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3444944 · Cited by 568
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
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 ›
Are you an author, or do you see a problem with this page? Request a review
