Causal Inference: Bringing together data science and causal inference for better policy recommendations

The reams of data collected on human activity every minute of every day from websites and sensors, and hospitals and government agencies need to be analyzed and explained to help policymakers improve their decision-making. Policymakers and legislators seek guidance in various areas from public health to law. However, when not carefully used, all data collected on human behaviour may lead to harmful recommendations if based on spurious correlations between various data sets rather than solid analysis establishing a causal relationship between a particular intervention and the outcomes visible in the statistics. The causal channels are essential to lead policymakers in their decisions process, and very often, the questions asked by policymakers to researchers are causal.

Causal Inference, a DSI Emerging Data Science Program, is dedicated to promoting cross-disciplinary exchange and collaboration among applied researchers, data scientists, and experts in causality. By attracting diverse attendees and encouraging mutual learning, this Program empowers researchers to harness causal inference and data science as powerful drivers of progress in their respective fields.  The Program aims to significantly impact the data sciences by advancing the state of the art in causal inference and its applications to real-world policy problems. By fostering collaboration and exchange of ideas between experts in data science and causal inference, and applied researchers, the Program seeks to address some key challenges in data science, such as algorithm fairness, confounding variables bias, and the need for more robust statistical inference methods.

The Causal Inference Program creates an inclusive space for conversations across diverse disciplines, where researchers expose their research questions, data limitations, and challenges related to causal methods.  Experts in data sciences and causality will introduce new and existing methods, encouraging the pursuit of research goals. Applied researchers will present key limitations informed by practice, jointly addressing the barriers to using current methods in solving policy problems of our time. Core activities consist of workshops and a monthly lecture series sharing the state of the literature with a non-specialized audience.

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Events and opportunities


Data scientists, experts in causal inferences, and empirical researchers working with policymakers will gather to present their work and discuss

November 10 – 11, 2023
Forging a Path: Causal Inference and Data Science for Improved Policy

Lecture Series

Focusing on sharing the state of the literature to a non-specialized audience


Gustavo J. Bobonis

Professor, Department of Economics, Munk School of Global Affairs and Public Policy, Faculty of Arts & Science, University of Toronto

Ismael Mourifié

Professor, Department of Economics, Faculty of Arts & Science, University of Toronto

Raji Jayaraman

Associate Professor, ESMT Berlin and Department of Economics, Faculty of Arts & Science, University of Toronto

Linbo Wang

Assistant Professor, Department of Statistical Sciences, Faculty of Arts & Science, University of Toronto Scarborough