arXiv 18 Oct 2023 · Statistics — Methodology
arXiv:2310.11969 · PDF · DOI · OpenAlex · Extracted main text
This paper proposes a simple, yet powerful, method for balancing distributions of covariates for causal inference based on observational studies. The method makes it possible to balance an arbitrary number of quantiles (e.g., medians, quartiles, or deciles) together with means if necessary. The proposed approach is based on the theory of calibration estimators (Deville and S\"arndal 1992), in particular, calibration estimators for quantiles, proposed by Harms and Duchesne (2006). The method does not require numerical integration, kernel density estimation or assumptions about the distributions. Valid estimates can be obtained by drawing on existing asymptotic theory. An illustrative example of the proposed approach is presented for the entropy balancing method and the covariate balancing propensity score method. Results of a simulation study indicate that the method efficiently estimates average treatment effects on the treated (ATT), the average treatment effect (ATE), the quantile treatment effect on the treated (QTT) and the quantile treatment effect (QTE), especially in the presence of non-linearity and mis-specification of the models. The proposed approach can be further generalized to other designs (e.g. multi-category, continuous) or methods (e.g. synthetic control method). An open source software implementing proposed methods is available.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Sant'Anna, P. H., Song, X., and Xu, Q (2022) Covariate distribution balance via propensity scores | 1.000 | 11 | 4 | 100% |
| 2 | Imai, K. and Ratkovic, M (2014) Covariate balancing propensity score | 1.000 | 6 | 3 | 100% |
| 3 | Hainmueller, J (2012) Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies | 1.000 | 5 | 4 | 100% |
| 4 | Deville, J.-C. and Särndal, C.-E (1992) Calibration estimators in survey sampling | 0.874 | 5 | 2 | 100% |
| 5 | Harms, T. and Duchesne, P (2006) On calibration estimation for quantiles | 0.843 | 3 | 3 | 100% |
| 6 | Benjamin, D. J (2003) Does 401 (k) eligibility increase saving?: Evidence from propensity score subclassification | 0.644 | 2 | 2 | 100% |
| 7 | Fong, C., Hazlett, C., and Imai, K (2018) Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements | 0.644 | 2 | 2 | 100% |
| 8 | Gunsilius, F. F (2023) Distributional Synthetic Controls | 0.644 | 2 | 2 | 100% |
| 9 | Hazlett, C (2020) Kernel Balancing: A Flexible Non-Parametric Weighting Procedure for Estimating Causal Effects | 0.644 | 2 | 2 | 100% |
| 10 | Greifer, N (2023) WeightIt: Weighting for Covariate Balance in Observational Studies | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 31 scored citations.