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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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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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