Forging A Path: Causal Inference and Data Science for Improved Policy Workshop

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 econometric tools and relevant economic questions addressing societies’ most pressing concerns. This in-person 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. In the spirit of working across multiple disciplines and employing a variety of methodologies, the DSI Causal Inference Emerging Data Science Program is in collaboration with the Forward Society (FOS) Lab. 

This Workshop is part of the Causal Inference Emerging 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 in-person at the Workshop to foster collaborative exploration, amplifying the impact of causal inference and data science research on real-world policy challenges. 


November 10, 2023
9:00 – 9:30 am
Registration and Breakfast
9:30 – 9:40 am
Opening Remarks

Prof. Linbo Wang, Department of Statistical Sciences, Faculty of Arts & Science, University of Toronto
Prof. Gustavo Bobonis, Department of Economics, Faculty of Arts & Science, University of Toronto
9:40 – 10:20 am
Causal Inference with Deep Generative Model

Prof. Rahul G. Krishnan, Department of Computer Science, Faculty of Arts & Science, and Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto
10:20 – 11:00 am
How Can Novel Data Science Approaches Improve Causal Inference for Population Health?

Prof. Laura Rosella, Dalla Lana School of Public Health, and Associate Director, Education and Training, Data Sciences Institute, University of Toronto
11:00 – 11:20 am
11:20 – 12:00 pm
Deconstructing Risk in Predictive Risk Models for Human-Centred Causal Inferences

Prof. Shion Guha, Faculty of Information, University of Toronto
12:00 – 1:30 pm
1:30 – 2:10 pm
The Unclaimed Property Puzzle: Billion Dollar Bills Lying on the Sidewalk

Prof. Eva Vivalt, Department of Economics, Faculty of Arts & Science, University of Toronto
2:10 – 2:50 pm
Methods for Counterfactual Data Augmentation in Reinforcement Learning

Prof. Elliot Creager, Department of Electrical and Computer Engineering, University of Waterloo
2:50 – 3:10 pm

3:10 – 4:10 pm
Estimating the Value of Evidence-Based Decision Making - Keynote

Prof. Alberto Abadie, Department of Economics, MIT
4:10 – 4:20 pm
Concluding Remarks

Prof. Ismael Mourifié, Department of Economics, Faculty of Arts & Science, University of Toronto
4:20 – 5:30 pm
Refreshments and Social Hour
November 11, 2023
9:00 – 9:30 am
9:30 – 10:30 am
Estimating Causal Effects Under Interference and Implications for Policy - Keynote

Prof. Elizabeth Halloran, Professor, Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, and Department of Biostatistics, University of Washington
10:30 – 10:40 am
10:40 – 11:20 am
The Effects of School Consolidation on Students and Teachers: Evidence from an Underperforming System

Prof. Gustavo Bobonis, Department of Economics, Faculty of Arts & Science, University of Toronto
11:20 – 12:00 pm
Student-Led Roundtable: Wrap Up and Next Steps

Vahid Balazadeh, Sonia Markes, Stephen Tino, Dario Toman, Atom Vayalinkal
12:00 – 1:30 pm

The event is finished.

Local Time

  • Timezone: America/New_York
  • Date: Nov 10 - 11 2023


10th floor, 700 University Avenue, Toronto