Causal Inference for Networked and Complex Systems
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 aims to bridge cutting‑edge research in causal inference with real‑world policy applications in networked and complex systems. Speakers will highlight advances in econometrics, statistics, and machine learning that address challenges such as interference, complex dependence structures, and high‑dimensional data. Through talks spanning economics, data science, and healthcare, the event will emphasize how modern causal methods can generate credible evidence for policy and decision‑making in practice.
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
Fabrizia Mealli, Professor, Department of Economics, European University Institute (EUI)
Rajesh Ranganath, Assistant Professor, Courant Institute of Mathematical Science, NYU
Max Tabord-Meehan, Associate Professor, Department of Economics, University of Toronto