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.
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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
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