| Journal Title /掲載ジャーナル名 |
Journal of the American Statistical Association |
| Publication Year and Month /掲載年月 |
November, 2023 |
| Paper Title /論文タイトル |
Causal Inference with Noncompliance and Unknown Interference |
| DOI /論文DOI |
10.1080/01621459.2023.2284413 |
| Author of Waseda University /本学の著者 |
HOSHINO, Tadao(Associate Professor, Faculty of Political Science and Economics, School of Political Science and Economics):First Author |
| Related Websites /関連Web |
– |
| Abstract /抄録 |
We consider a causal inference model in which individuals interact in a social network and they may not comply with the assigned treatments. In particular, we suppose that the form of network interference is unknown to researchers. To estimate meaningful causal parameters in this situation, we introduce a new concept of exposure mapping, which summarizes potentially complicated spillover effects into a fixed dimensional statistic of instrumental variables. We investigate identification conditions for the intention-to-treat effects and the average treatment effects for compliers, while explicitly considering the possibility of misspecification of exposure mapping. Based on our identification results, we develop nonparametric estimation procedures via inverse probability weighting. Their asymptotic properties, including consistency and asymptotic normality, are investigated using an approximate neighborhood interference framework. For an empirical illustration, we apply our method to experimental data on the anti-conflict intervention school program. The proposed methods are readily available with the companion R package latenetwork. |
