Causal inference under spillover and contagion: structural versus agnostic methods
Forrest Crawford, Yale School of Public Health
Friday, September 20, 2019 - 3:30pm
Two competing paradigms dominate statistical and econometric approaches to estimating the effects of interventions in interconnected/interacting groups under spillover or interference between experimental units. “Mechanistic” or “structural” models capture dynamic features of the process by which outcomes are generated, permitting inferences with real-world interpretations and detailed predictions. "Agnostic", “design-based”, or “reduced form” approaches, often based on notions of randomization, refrain from specifying the full joint distribution of the data, and provide inferences that are robust to model mis-specification. Statisticians, economists, epidemiologists, and other scientists often disagree about which of these paradigms is superior for studies of interventions among potentially interacting individuals, with competing claims about model realism, bias, and credibility of inferences. In this presentation, I review methods for estimating the causal effect of an individualistic treatment under spillover, with special attention to the case of contagion, whereby units can transmit their outcome to others in a way that depends on their treatment. I define a formal structural model of contagion, and ask what causal features agnostic or reduced-form estimates recover. I exhibit analytically and by simulation the circumstances under which coefficients in a marginal regression model imply an effect whose direction is opposite that of the true individualistic treatment effect. Furthermore, I show that widely recommended randomization designs and estimators may provide misleading inferences about the direct effect of an intervention when outcomes are contagious. These ideas are illustrated in three empirical examples: transmission of tuberculosis, product adoption, and peer recruitment in social networks.
Bio: Forrest W. Crawford PhD is Associate Professor, Department of Biostatistics, Department of Statistics & Data Science, Yale School of Management (Operations), and Department of Ecology & Evolutionary Biology, Yale University. He is affiliated with the Center for Interdisciplinary Research on AIDS, the Institute for Network Science, the Computational Biology and Bioinformatics program, and the Public Health Modeling concentration. He is the recipient of the NIH Director's New Innovator Award and a Yale Center for Clinical Investigation Scholar Award. His research interests include causal inference, networks, graphs, stochastic processes, and optimization for applications in epidemiology, public health, and social science.
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