arXiv 10 Dec 2025 · Statistics — Methodology
arXiv:2512.09337 · PDF · DOI · OpenAlex · Extracted main text
This paper develops methods for estimating the natural direct and indirect effects in causal mediation analysis. The efficient influence function-based estimator (EIF-based estimator) and the inverse probability weighting estimator (IPW estimator), which are standard in causal mediation analysis, both rely on the inverse of the estimated propensity scores, and thus they are vulnerable to two key issues (i) instability and (ii) finite-sample covariate imbalance. We propose estimators based on the weights obtained by an algorithm that directly penalizes weight dispersion while enforcing approximate covariate and mediator balance, thereby improving stability and mitigating bias in finite samples. We establish the convergence rates of the proposed weights and show that the resulting estimators are asymptotically normal and achieve the semiparametric efficiency bound. Monte Carlo simulations demonstrate that the proposed estimator outperforms not only the EIF-based estimator and the IPW estimator but also the regression imputation estimator in challenging scenarios with model misspecification. Furthermore, the proposed method is applied to a real dataset from a study examining the effects of media framing on immigration attitudes.
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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 | Tchetgen Tchetgen, Eric J. and Shpitser, Ilya (2012) Semiparametric Theory for Causal Mediation Analysis: Efficiency bounds, multiple robustness, and sensitivity analysis | 1.000 | 11 | 4 | 100% |
| 2 | Wang, Yixin and Zubizarreta, Jose R (2019) Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations | 0.916 | 13 | 6 | 77% |
| 3 | Chan, K. C. G. and Imai, K. and Yam, S. C. P. and Zhang, Z (2016) Efficient nonparametric estimation of causal mediation effects | 0.737 | 3 | 2 | 100% |
| 4 | Chan, Kwun Chuen Gary and Yam, Sheung Chi Phillip and Zhang, Zheng (2016) Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting | 0.737 | 3 | 2 | 100% |
| 5 | Chattopadhyay, Ambarish and Hase, Christopher H. and Zubizarreta, Jo… (2020) Balancing vs modeling approaches to weighting in practice | 0.737 | 3 | 2 | 100% |
| 6 | Brader, Ted and Valentino, Nicholas and Suhay, Elizabeth (2008) What triggers public opposition to immigration? Anxiety, group cues, and immigration threat | 0.737 | 3 | 2 | 100% |
| 7 | Chen, Xiaohong (2007) Chapter 76: Large sample sieve estimation of semi-nonparametric models | 0.644 | 2 | 2 | 100% |
| 8 | Fan, Jianqing and Imai, Kosuke and Lee, Inbeom and Liu, Han and Ning… (2023) Optimal covariate balancing conditions in propensity score estimation | 0.644 | 2 | 2 | 100% |
| 9 | Farbmacher, Helmut and Huber, Martin and Lafférs, Luká s and Langen,… (2022) Causal mediation analysis with double machine learning | 0.644 | 2 | 2 | 100% |
| 10 | Imai, Kosuke and Keele, Luke and Yamamoto, Teppei (2010) Identification, Inference and Sensitivity Analysis for Causal Mediation Effects | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 43 scored citations.