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Efficient difference-in-differences estimation under partial interference with incremental propensity score policies

Junjie Li, Yukitoshi Matsushita

arXiv 22 Jul 2026 · Econometrics

arXiv:2607.19925 · PDF · Extracted main text

Abstract

This paper develops efficient difference-in-differences (DID) estimation under partial interference with a cluster incremental propensity score (CIPS) policy. We define direct and spillover average treatment effects on the treated, establish their identification, and derive their efficient influence functions, from which we construct a cross-fitted estimator. Simulations confirm its finite-sample validity, and an application to China's New Rural Pension Scheme uncovers a significantly negative within-household spillover of pension participation on co-residents' labour income.

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Park, Chan and Kang, Hyunseung (2022) Efficient semiparametric estimation of network treatment effects under partial interference0.9568488%
2Lee, Chanhwa and Zeng, Donglin and Hudgens, Michael G (2025) Efficient nonparametric estimation of stochastic policy effects with clustered interference0.93511482%
3Sant’Anna, Pedro HC and Zhao, Jun (2020) Doubly robust difference-in-differences estimators0.84333100%
4Liu, Lan and Hudgens, Michael G and Saul, Bradley and Clemens, John… (2019) Doubly robust estimation in observational studies with partial interference0.81142100%
5Kennedy, Edward H (2019) Nonparametric causal effects based on incremental propensity score interventions0.73732100%
6Tchetgen, Eric J Tchetgen and VanderWeele, Tyler J (2012) On causal inference in the presence of interference0.73732100%
7Huang, Wei and Zhang, Chuanchuan (2021) The Power of Social Pensions: Evidence from China's New Rural Pension Scheme0.69381100%
8Hudgens, Michael G and Halloran, M Elizabeth (2008) Toward causal inference with interference0.64422100%
9Sun, Kuan and Xiao, Zhiguo (2025) Difference-in-Differences Under Network Interference0.64422100%
10Chernozhukov, Victor and Chetverikov, Denis and Demirer, Mert and Du… (2018) Double/debiased machine learning for treatment and structural parameters0.40511100%

Showing the top 10 of 14 scored citations.