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Abstract Details
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Activity Number:
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284
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Social Statistics Section
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| Abstract - #304214 |
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Title:
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Estimating Causal Effects Under General Interference and Clustering
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Author(s):
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Peter M. Aronow*+ and Cyrus Samii and Joel Middleton
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Companies:
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Yale University and New York University and New York University
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Address:
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53 Barkers Point Road, Sands Point, NY, 11050, United States
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Keywords:
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interference ;
causal inference ;
randomized experiments ;
group-randomized trials ;
randomization inference ;
sampling theory
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Abstract:
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We present simple, unbiased design-based estimators of average unit-level treatment effects under arbitrary interference and clustering. Weakly conservative estimators for the randomization variance of the average treatment effects estimators are presented, as are conditions that justify confidence intervals based on a normal approximation. Efficiency-enhancing refinements based on the Hajek estimator, constant effects variance estimators, and covariate adjustment are developed. The methods are used to estimate peer influence effects in a recent experiment studying the spread of norms against prejudice as well as social pressure effects in a voter mobilization experiment in the United States. Applications relevant to research in environmental protection, "viral marketing," two-stage trials, and stepped-wedge designs are also discussed.
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