In an era where data-driven insights fuel innovation and inform decisions, policymakers and stakeholders increasingly seek guidance in research from various areas such as criminal justice, health, and labour law. However, the wealth of data gathered to understand human behaviour can lead to misguided recommendations if not approached appropriately during the analysis phase. This challenge has inspired the question: How can we elevate the quality of data analysis to better inform decision-making?
Funded through the Emergent Data Sciences Program competition, University of Toronto Professors Linbo Wang (Department of Statistical Sciences, Faculty of Arts & Science, University of Toronto Scarborough), Gustavo J. Bobonis (Department of Economics, Faculty of Arts & Science), Ismael Mourifié (Department of Economics, Faculty of Arts & Science), and Raji Jayaraman (Department of Economics, Faculty of Arts & Science), are co-leading Bringing Together Data Science and Causal Inference for Better Policy Recommendations.
The program promotes cross-disciplinary exchange and collaboration among experts in data science, causal inference, and applied research. Its overarching mission is to influence the landscape of data sciences by advancing the state of the art in causal inference and its applications to real-world policy problems. The program aims to tackle key challenges in data science, including algorithmic fairness, bias from confounding variables, and the need for more robust statistical inference methods.
The program aims to achieve this by creating an inclusive forum for discussions across diverse disciplines. Here, researchers will get to share 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.
Featured activities include three workshops and a lecture series on causality. In these workshops, data scientists, causal inference experts, and empirical researchers collaborating with policymakers convene to present their work. The lecture series focuses on sharing the state of literature with a non-specialized audience. The first workshop, Forging a Path: Casual Inference and Data Science for Improved Policy, is scheduled for November 10-11.
“Our collaborative effort will enable us to address pressing policy questions with a newfound depth, ensuring that data-driven decisions are rooted in robust causal understanding,” say Professors Ismael Mourifié and Linbo Wang. “We look forward to working alongside fellow experts to drive meaningful impact in both academia and policymaking.”
Recipients of the DSI Emergent Data Sciences Program competition are funded for their programs, which foster the development of innovative data science methodologies, deep connections with computation and applied disciplines, new training programs, collaboration, knowledge mobilization, and impact beyond academia.
“This Emerging Data Science program exemplifies DSI’s commitment to fostering collaboration and innovation in data science research. It reflects our dedication to addressing complex challenges at the intersection of data analysis and real-world policymaking. We are confident that this initiative will have an impact,” says David Lie, Associate Director of Thematic Programming & Data Access, Data Sciences Institute.