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

Please note: the event has ended. The info is being left up for reference.
Stay tuned for next year’s event.

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. 

Program

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
Break
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
Lunch
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
Break

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
Breakfast
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
Break
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
Lunch

Keynote

Prof. Alberto Abadie
Department of Economics, MIT

Alberto Abadie is an econometrician and empirical microeconomist with broad disciplinary interests. Professor Abadie received his Ph.D. in Economics from MIT in 1999. Upon graduating, he joined the faculty at the Harvard Kennedy School, where he was promoted to a full professor in 2005. He returned to MIT in 2016, where he is Professor of Economics and Associate Director of the Institute for Data, Systems, and Society (IDSS).

Prof. Elizabeth Halloran  
Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Center, and Department of Biostatistics, University of Washington

Elizabeth “Betz” Halloran is a world leader in using mathematical and statistical methods to study infectious diseases and a pioneer in the design and analysis of vaccine studies. She is director of the Center for Inference and Dynamics of Infectious Diseases. Headquartered at Fred Hutch, this center helps the federal government understand and prepare for infectious-disease outbreaks. She is also head of the Program in Biostatistics, Bioinformatics and Epidemiology in Fred Hutch’s Vaccine and Infectious Disease Division. Her work is used to develop strategies to stop outbreaks of serious global threats such as Zika virus disease, Ebola virus disease, influenza, COVID-19, cholera, and dengue fever.

Speakers

Prof. Gustavo Bobonis Department of Economics, Faculty of Arts & Science, University of Toronto

Gustavo J. Bobonis holds the Canada Research Chair in the Political Economy of Development. His research focuses on economic development in the Americas and its relationship to politics and policy. His most recent work focuses on evaluating barriers to good government by investigating the complex relationships between voter patronage, poverty, local customs, and government responsiveness, and interventions to improve educational outcomes and women’s wellbeing in the context of Puerto Rico.

Prof. Elliot Creager Department of Electrical and Computer Engineering, University of Waterloo

Elliot Creager is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. He is also a Faculty Affiliate at the Vector Institute for Artificial Intelligence and the Schwartz Reisman Institute for Technology and Society. He works on a variety of topics within machine learning, more specifically in the areas of algorithmic fairness, representation learning, and robustness. Professor Creager received his PhD from the University of Toronto and was an intern and student researcher at Google Brain in Toronto during his graduate studies.

Prof. Shion Guha
Faculty of Information, University of Toronto

Shion Guha is cross appointed between the Faculty of Information and Department of Computer Science. He directs the Human-Centered Data Science Lab and am part of the broader Critical Computing research community. His research interests are broadly concerned with the nascent field of Human-Centered Data Science that he has helped to develop. He is interested in algorithmic decision-making, especially in public services, as well as the intersection between AI and public policy.

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

Rahul Krishnan received his PhD from MIT advised by Prof. David Sontag where he was part of the Clinical ML Group. He received a BASc in Computer Engineering at the University of Toronto and an MS from New York University. Rahul was previously a senior researcher at Microsoft Research New England. He is expanding the Artificial Intelligence education in LMP (alongside Dr. Bo Wang) and has recently launched a new graduate course on Machine Learning for Healthcare.

Prof. Ismael Mourifié Department of Economics, Faculty of Arts & Science, University of Toronto

Ismael Mourifié’s research explores questions related to econometrics and labour and has developed and applied methods to analyse women’s choice of university major and underrepresentation in STEM, improved methods for treatment evaluation, and more. Dr. Mourifié is also a research associate of the National Bureau of Economic Research (NBER). He received his PhD in Economics from the University of Montréal.

Prof. Laura Rosella
 Dalla Lana School of Public Health, and Associate Director, Education and Training, Data Sciences Institute, University of Toronto

Laura Rosella is the Principal Investigator and Scientific Director of the Population Health Analytics Lab. She holds the Canada Research Chair in Population Health Analytics. In 2020, she was made the Inaugural Stephen Family Research Chair in Community Health at the Institute for Better Health, Trillium Health Partners. Her additional scientific appointments include the Vector Institute and Site Director for ICES UofT.

Prof. Eva Vivalt
Department of Economics, Faculty of Arts & Science, University of Toronto

Eva Vivalt’s research explores questions related to development and labour, and has studied cash transfers in the U.S., how to improve evidence-based policy and decision-making, and more. Dr. Vivalt is also the Director of the Global Priorities Institute at the University of Oxford, and co-founder of the Social Science Prediction Platform, a platform to coordinate the collection of forecasts of research results. She received her PhD in Economics from the University of California, Berkeley.

Prof. Linbo Wang Department of Statistical Sciences, Faculty of Arts & Science, University of Toronto

Linbo Wang received his PhD in Biostatistics from University of Washington in 2016. Prior to joining the University of Toronto, he spent two years at Harvard Causal Inference Program. His research interest includes causal inference, graphical models, and modern statistical inference in infinite-dimensional models. He is the recipient of several research awards, including a NSERC Discovery Accelerator Supplement in 2019.

November 10 – 11, 2023
In-person only
10th floor seminar room 
700 University Avenue,
Toronto, ON

Register Here