Causal Inference Across Fields: Methods, Insights, and Applications
In the evolving landscape of statistical, econometric, and data science advancements, a significant number of innovative methodologies remain untapped by applied research. There is a disconnect between cutting-edge statistical tools and applied research questions addressing societies’ most pressing concerns.
This workshop seeks to address this gap and establish research collaboration between data scientists, experts in the causal inference literature, and applied researchers who better understand the empirical contexts, objectives, and challenges faced by policymakers. Our proposed program will facilitate a cross-disciplinary exchange.
Funded through the DSI Emergent Data Sciences Program competition, and co-led by Professors Linbo Wang (Departments of Statistical Sciences and Computer Science), Rahul G. Krishnan (Dept. of Computer Science), Gustavo J. Bobonis, and Raji Jayaraman (Department of Economics) at the University of Toronto.
Join us for in-person discussions on bridging causal inference and data science with real-world policy challenges.
Speakers
Elias Bareinboim, Associate Professor, Department of Computer Science and Director, Causal Artificial Intelligence Lab, Columbia University
Parag Pathak, Professor of Economics at MIT, founding co-director of MIT’s Blueprint Labs and the NBER Working Group on Market Design
Alicia Modestino, Associate Professor, School of Public Policy and Urban Affairs and Department of Economics, Northeastern University
Kuan Liu, Assistant Professor, Health Services Research Program, Institute of Health Policy, Management and Evaluation, University of Toronto