Imai, Kosuke, Zhichao Jiang, and Anup Malani. (2021). ``Causal Inference with Interference and Noncompliance in Two-Stage Randomized Experiments.'' Journal of the American Statistical Association, Vol. 116, No. 534, pp. 632-644.
In many social science experiments, subjects often interact with each other and as a result one unit’s treatment influences the outcome of another unit. Over the last decade, a significant progress has been made towards causal inference in the presence of such interference between units. Researchers have shown that the two-stage randomization of treatment assignment enables the identification of average direct and spillover effects. However, much of the literature has assumed perfect compliance with treatment assignment. In this paper, we establish the nonparametric identification of the complier average direct and spillover effects in two-stage randomized experiments with interference and noncompliance. In particular, we consider the spillover effect of the treatment assignment on the treatment receipt as well as the spillover effect of the treatment receipt on the outcome. We propose consistent estimators, and derive their randomization-based variances under the stratified interference assumption. We also prove the exact relationships between the proposed randomization-based estimators and the popular two-stage least squares estimators. The proposed methodology is motivated by and applied to our own randomized evaluation of the India’s National Health Insurance Program (RSBY), where we find some evidence of spillover effects. The proposed methods are implemented via an open-source software package . |
Malani, Anup, Cynthia Kinnan, Gabriella Conti, Kosuke Imai, Morgen Miller, Shailender Swaminathan, Alessandra Voena, and Bartosz Woda
``Evaluating and Pricing Health Insurance in Lower-Income Countries: A Field Experiment in India.''
American Economic Journal: Economic Policy, Forthcoming.
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Zhou, Lingxiao, Kosuke Imai, Jason Lyall, and Georgia Papadogeorgou
(2026).
``Dynamic Policy Evaluation and Learning with Spatio-temporal Data.''
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Lo, Adeline, Santiago Olivella, and Kosuke Imai
(2026).
``A Statistical Model of Bipartite Networks: Application to Cosponsorship in the United States Senate.''
Political Analysis, Vol. 34, No. 3 (July 2026), pp. 451-470.
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Sengupta, Souhardya, Kosuke Imai, and Georgia Papadogeorgou
(2025).
``Low-rank Covariate Balancing Estimators under Interference.''
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Chattopadhyay, Ambarish, Kosuke Imai, and Jose R. Zubizarreta
(2024).
``Design-based inference for generalized network experiments with stochastic interventions.''
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Jiang, Zhichao, Kosuke Imai, and Anup Malani
(2023).
``Statistical Inference and Power Analysis for Direct and Spillover Effects in Two-Stage Randomized Experiments.''
Biometrics, Vol. 79, No. 3 (September), pp. 2370-2381.
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Bertsimas, Dimitris, Kosuke Imai, and Michael Lingzhi Li
(2023).
``Distributionally Robust Causal Inference with Observational Data.''
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Olivella, Santiago, Tyler Pratt, and Kosuke Imai
(2022).
``Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts.''
Journal of the American Statistical Association, Vol. 117, No. 539, pp. 1068-1081.
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Imai, Kosuke, and Zhichao Jiang
(2020).
``Identification and Sensitivity Analysis of Contagion Effects in Randomized Placebo-Controlled Trials.''
Journal of the Royal Statistical Society, Series A (Statistics in Society), Vol. 183, No. 4 (October), pp. 1637-1657.
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Kim, In Song, Steven Liao, and Kosuke Imai
(2020).
``Measuring Trade Profile with Granular Product-level Trade Data.''
American Journal of Political Science, Vol. 64, No. 1 (January), pp. 102-117.
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