CAM Colloquium: Dean Eckles (Sloan School of Management and Institute for Data, Systems & Society, MIT) - Noise-induced randomization in regression discontinuity designs

Description

Abstract:
In trying to draw credible causal inferences, we typically either randomize units to treatments or posit that nature has done so for us, perhaps conditional on observable characteristics of the units. However, one popular and arguably quite credible research design—the regression discontinuity design (RDD)—seemingly involves deterministic assignment to treatment, or it at least has been difficult to characterize it as a randomized experiment on any non-infinitesimal set of units. In an RDD, treatment is determined by whether an observed running variable crosses a pre-specified threshold. While sometimes described as akin to a locally randomized experiment in a neighborhood of the threshold, standard formal analyses do not make reference to probabilistic treatment assignment and instead identify treatment effects via continuity arguments. Here we propose a new approach to identification, estimation, and inference in regression discontinuity designs that exploits measurement error in the running variable. Under an assumption that the measurement error is exogenous, we show how to consistently estimate causal effects using a class of linear estimators that weight treated and control units so as to balance a latent variable of which the running variable is a noisy measure. We find this approach to facilitate identification of both familiar estimands from the literature, as well as policy-relevant estimands that correspond to the effects of realistic changes to the existing treatment assignment rule. We demonstrate the method with a study of retention of HIV patients and evaluate its performance using simulated data and a regression discontinuity design artificially constructed from test scores in early childhood.

Joint work with Nikolaos Ignatiadis, Stefan Wager, and Han Wu. https://arxiv.org/abs/2004.09458

Bio:
Dean Eckles is a social scientist and statistician. At the Massachusetts Institute of Technology, Dean is the Mitsubishi Career Development Professor, an associate professor in the MIT Sloan School of Management, and affiliated faculty at the Institute for Data, Systems & Society in the MIT Schwarzman College of Computing. He was previously a scientist at Facebook and Nokia. Much of his research examines how interactive technologies affect human behavior by mediating, amplifying, and directing social influence — and statistical methods to study these processes. Dean’s empirical work uses observational studies and field experiments involving hundreds of millions of people. His published papers appear in Proceedings of the National Academy of Sciences, Journal of the American Statistical Association, Science, and other peer-reviewed journals and proceedings in statistics, computer science, and marketing. Dean completed five degrees, including his PhD, at Stanford University.

Zoom Link Access:
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