Edward McFowland III (Harvard Business School), "Anomalous Pattern Detection: A Novel Lens for Scientific Inquiry"
There has been a growing interest in the use of machine learning methods for causal inference, which often involves adjusting or reappropriating predictive models, with causality in mind. As an alternative, anomaly detection methods offer a unique lens through which to conduct causal inference, as the presence of a causal effect results in treatment group units that appear anomalous in comparison to the control group. Moreover, anomalous pattern detection intentionally localizes the presence of treatment effects, which has tremendous value when the ultimate goal involves hypothesis generation, understanding causal mechanisms, or targeting subpopulations. As motivation, we will consider the identification of subpopulations in randomized experiments with extremely significant effects, and will consider other quasi-experimental settings as time permits.
The Applied Statistics Workshop (Gov 3009) meets all academic year, Wednesdays, 12pm-1:30pm, in CGIS K354. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers.
More information is available at the Gov 3009 website: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009