Causal Inference and Overparameterized Autoencoders in the Light of Drug Repurposing for SARS-CoV-2

Abstract:  Massive data collection holds the promise of a better understanding of complex phenomena and ultimately, of better decisions. An exciting opportunity in this regard stems from the growing availability of perturbation / intervention data (drugs, knockouts, overexpression, etc.) in biology. In order to obtain mechanistic insights from such data, a major challenge is the development of a framework that integrates observational and interventional data and allows predicting the effect of yet unseen interventions or transporting the effect of interventions observed in one context to another. I will present a framework for causal inference based on such data and particularly highlight the role of overparameterized autoencoders. We end by demonstrating how these ideas can be applied for drug repurposing in the current SARS-CoV-2 crisis.

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Bio: Caroline Uhler is the Henry L. and Grace Doherty associate professor in EECS (Electrical Engineering & Computer Science) and IDSS (Institute for Data, Systems and Society). She is a member of LIDS (Laboratory for Information and Decision Systems), the Center for Statistics, Machine Learning at MIT, and the ORC (Operations Research Center).

She is an elected member of the International Statistical Institute and a recipient of a Simons Investigator Award, a Sloan Research Fellowship, an NSF Career Award, a Sofja Kovalevskaja Award from the Humboldt Foundation, and a START Award from the Austrian Science Foundation.