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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, Phoebe Holtzman, Kosuke
Imai, Cynthia Kinnan, Morgen Miller, Shailender Swaminathan,
Alessandra Voena, Bartosz Woda, and Gabriella Conti. ``Effect of Health Insurance in India:
A Randomized Controlled Trial.'' |
Jiang, Zhichao and Kosuke Imai. (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. |
Zhang, Yi and Kosuke Imai. ``Individualized Policy Evaluation and
Learning under Clustered Network Interference.''
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Chattopadhyay, Ambarish, Kosuke Imai, and
Jose R. Zubizarreta. ``Design-based inference for generalized
network experiments with stochastic interventions.''
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Huang, Karissa, Zhichao Jiang, and Kosuke
Imai. ``RCT2:
Designing and Analyzing Two-Stage Randomized
Experiments.'' available through The Comprehensive R
Archive Network and GitHub.
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