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

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  • Junjie Li
  • Yukitoshi Matsushita

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 evaluate its finite-sample performance. An application to China's New Rural Pension Scheme recovers the reduction in farmwork among pension recipients reported by the original county-level analysis, separately estimates a within-household spillover alongside the direct effect, and traces both effects across the policy parameter.

Suggested Citation

  • Junjie Li & Yukitoshi Matsushita, 2026. "Efficient difference-in-differences estimation under partial interference with incremental propensity score policies," Papers 2607.19925, arXiv.org, revised Sep 2026.
    • Handle: RePEc:arx:papers:2607.19925
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    References listed on IDEAS

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    1. Edward H. Kennedy, 2019. "Nonparametric Causal Effects Based on Incremental Propensity Score Interventions," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 114(526), pages 645-656, April.
    2. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    3. Callaway, Brantly & Karami, Sonia, 2023. "Treatment effects in interactive fixed effects models with a small number of time periods," Journal of Econometrics, Elsevier, vol. 233(1), pages 184-208.
    4. Chanhwa Lee & Donglin Zeng & Michael G. Hudgens, 2025. "Efficient Nonparametric Estimation of Stochastic Policy Effects with Clustered Interference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 120(549), pages 382-394, January.
    5. Jinyong Hahn, 1998. "On the Role of the Propensity Score in Efficient Semiparametric Estimation of Average Treatment Effects," Econometrica, Econometric Society, vol. 66(2), pages 315-332, March.
    6. Hudgens, Michael G. & Halloran, M. Elizabeth, 2008. "Toward Causal Inference With Interference," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 832-842, June.
    7. Iván Díaz Muñoz & Mark van der Laan, 2012. "Population Intervention Causal Effects Based on Stochastic Interventions," Biometrics, The International Biometric Society, vol. 68(2), pages 541-549, June.
    8. C Park & H Kang, 2022. "Efficient semiparametric estimation of network treatment effects under partial interference [Multivariate binary discrimination by the kernel method]," Biometrika, Biometrika Trust, vol. 109(4), pages 1015-1031.
    9. Wei Huang & Chuanchuan Zhang, 2021. "The Power of Social Pensions: Evidence from China's New Rural Pension Scheme," American Economic Journal: Applied Economics, American Economic Association, vol. 13(2), pages 179-205, April.
    10. Kyle Butts, 2021. "Difference-in-Differences Estimation with Spatial Spillovers," Papers 2105.03737, arXiv.org, revised Jun 2023.
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