Estimating Average Causal Effects Under General Interference
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چکیده
This paper presents randomization-based methods for estimating average causal effects under arbitrary interference of known form. Conservative estimators of the randomization variance of the average treatment effects estimators are presented, as is justification for confidence intervals based on a normal approximation. Examples relevant to research in environmental protection, networks experiments, “viral marketing,” two-stage disease prophylaxis trials, and stepped-wedge designs are presented.
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تاریخ انتشار 2012