Causal Inference Across Fields: Methods, Insights, & 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.
Speakers
Melissa Dell, Andrew E. Furer Professor, Department of Economics, Harvard University
Dean C. Knox, Assistant Professor of Operations, Information, and Decisions, Assistant Professor of Statistics and Data Science, The Wharton School, University of Pennsylvania
Ashesh Rambachan, Assistant Professor, Department of Economics, Massachusetts Institute of Technology