Causal Inference across Fields: Methods, Insights, and Applications

Causal Inference across Fields: Methods, Insights, and Applications aims to bridge cutting-edge research with real-world policy applications.

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.

October 22, 2025
9:30-9:45	Registration
9:45-10:00	Opening Remarks Gustavo J. Bobonis, Professor, Department of Economics, Munk School of Global Affairs and Public Policy, Faculty of Arts & Science, University of Toronto
10:00-11:00	A Unifying Framework for Robust and Efficient Inference with Unstructured Data Melissa Dell, Professor, Department of Economics, Harvard University
11:00-12:00	*Assessing Counterfactual Fairness in Policing with Imperfect Proxies of Civilian Behavior* Dean C. Knox, Assistant Professor of Operations, Information, and Decisions, Assistant Professor of Statistics and Data Science, The Wharton School, University of Pennsylvania
12:00-1:15	Networking, poster session and Lunch Posters: CausalPFN: Amortized Causal Effect Estimation via In-Context Learning Vahid Balazadeh Department of Computer Science, Faculty of Arts & Science Bayesian Sensitivity of Causal Inference Estimators under Evidence-Based Priors Nikita Dhawan Department of Computer Science, Faculty of Arts & Science *Marginal Causal Effect Estimation with Continuous Instrumental Variables* Mei Dong Division of Biostatistics, Dalla Lana School of Public Health *Nonparametric Identification of Dynamic Treatment Effects* Chenyue Liu Department of Economics, Faculty of Arts & Science *Bayesian Latent Class Approach for Causal Estimation with Multiple Cognitive Outcomes* Emmett Peng Division of Biostatistics, Dalla Lana School of Public Health *Dynamic Factor Binary Panels: Identification and Particle Filter Estimation* Adrian Schroeder Department of Economics, Faculty of Arts & Science
1:15-2:15	*Program Evaluation with Remotely Sensed Outcomes* Ashesh Rambachan, Assistant Professor, Department of Economics, Massachusetts Institute of Technology
2:15-3:15	*Panel discussion*
3:15-3:30	*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

Speakers

Melissa Dell
Andrew E. Furer Professor of Economics, Department of Economics, Harvard University

Professor Dell is the 2020 recipient of the John Bates Clark Medal, awarded each year to an American economist under the age of forty who is judged to have made the most significant contribution to economic thought and knowledge. In 2018, The Economist named her one of the decade’s eight best young economists, and in 2014 she was named by the IMF as the youngest of 25 economists under the age of 45 shaping thought about the global economy. Her research focuses on economic growth, political economy, and the use of unstructured data (e.g., texts, images) in empirical analyses. She has examined the factors leading to the persistence of poverty and prosperity in the long run, the effects of trade-induced job loss on crime, the impacts of U.S. foreign intervention, and the effects of weather on economic growth. She has also developed deep learning powered methods for curating social science data at scale, supported by open-source packages such as Layout Parser, LinkTransformer, and EfficientOCR, and has developed methods for robust and efficient inference with unstructured data. Professor Dell is a senior scholar at the Harvard Academy for Area and International Studies and a research associate at the National Bureau of Economic Research.

Ashesh Rambachan
Silverman (1968) Family Career Development Assistant Professor of Economics at MIT

Professor Rambachan’s research interests are primarily in econometrics with a focus on applications of machine learning, focusing on algorithmic tools that drive decision-making in the criminal justice system and consumer lending markets and developing algorithmic procedures for discovering new behavioral models. Rambachan also develops methods for determining causation using cross-sectional and dynamic data.

Dean Knox

Assistant Professor, Operations, Information and Decisions, Assistant Professor of Statistics and Data Science, Wharton University of Pennsylvania

Professor Knox is a computational social scientist and an assistant professor at the Wharton School of the University of Pennsylvania (Operations, Information, and Decisions Department; Department of Statistics and Data Science). He develops statistical models and methods for complex social-science applications, including in public management and communication. He has advised the U.S. Department of Justice, the American Civil Liberties Union, and the NAACP Legal Defense Fund on civil-rights data analysis. Dean is the inaugural recipient of Science magazine’s NOMIS early career award for interdisciplinary research. His research has appeared in top general science and disciplinary flagship journals including Science, the Proceedings of the National Academy of Sciences, Nature Human Behavior, the Journal of the American Statistical Association, and the American Political Science Review.