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.
The Workshop is part of the DSI Causal Inference Emerging Data Science Emergent Data Science Program that aims to facilitate cross-disciplinary exchange, where applied researchers from different disciplines can present their research questions and methodological issues. In turn, data science and causality researchers explore new and existing methods while promoting their research agendas.
Join us to foster collaborative exploration, amplifying the impact of causal inference and data science research on real-world policy challenges.
| March 11, 2026 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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10:30-10:45 |
Registration
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10:45-11:00 |
Opening Remarks Linbo Wang, Associate Professor, Department of Statistical Sciences, Faculty of Arts & Science, University of Torontoto |
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11:00-12:00 |
Talk title Rajesh Ranganath, Associate Professor, Courant Institute of Mathematical Sciences, NYU |
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12:00-1:00 |
Networking and Lunch |
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1:00-2:00 |
On the Efficiency of Finely Stratified Experiments Max Tabord-Meehan, Associate Professor, Department of Economics, Faculty of Arts & Science, University of Toronto |
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2:00-3:00 |
Do Test Scores Help Teachers Give Better Track Advice to Students? A Principal Stratification Analysis
Fabrizia Mealli, Professor, Department of Economics, European University Institute |
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3:00-3:15 |
Closing remarks Rahul G. Krishnan, Assistant Professor, Department of Computer Science, Faculty of Arts & Science, and Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto |
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Fabrizia Mealli
Department of Economics, European University Institute
Professor Mealli’s research focuses on statistical and econometric methods for causal inference in experimental and observational settings, estimation techniques, simulation methods, missing data, and Bayesian inference, with applications to the social and biomedical sciences.
She is an Elected Fellow of the American Statistical Association (ASA) and sits on the Steering Committee of the European Causal Inference Meeting (EUROCIM). She is an Associate Editor for Biometrika, the Journal of the American Statistical Association T&M, The Annals of Applied Statistics, and Observational Studies. Mealli is President-elect of the Society for Causal Inference. Since 2001, Mealli has been teaching Causal Inference in International Schools and in Master and PhD programmes around the world.
Rajesh Ranganath
Courant Institute of Mathematical Sciences, NYU
Professor Ranganath’s research interests include causal, statistical, and probabilistic inference, out-of-distribution detection and generalization, deep generative modeling, interpretability, and machine learning for healthcare. Before joining NYU, he earned degrees in computer science; his PhD was completed at Princeton University working with Dave Blei, and his undergraduate was done at Stanford University. He has also spent time as a research affiliate at MIT’s Institute for Medical Engineering and Science.
Max Tabord-Meehan
Department of Economics, University of Toronto
Professor Max Tabord-Meehan’s research focuses on econometrics and causal inference, with particular interest in the analysis of randomized experiments.
He is an Associate Professor in the Department of Economics at the University of Toronto. He joined the department from the University of Chicago, where he taught since completing his PhD in Economics at Northwestern University in 2019.
