Causal Inference for Networked and Complex Systems

Data Sciences Institute

Data Science Institute, University of Toronto

This workshop aims to bridge cutting‑edge research in causal inference with real‑world policy applications in networked and complex systems. Speakers will highlight advances in econometrics, statistics, and machine learning that address challenges such as interference, complex dependence structures, and high‑dimensional data. Through talks spanning economics, data science, and healthcare, the event will emphasize how modern causal methods can generate credible evidence for policy and decision‑making in practice. The Workshop is part of the DSI <a href="https://datasciences.utoronto.ca/causal_inference/" title="https://datasciences.utoronto.ca/causal_inference/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">Causal Inference Emerging Data Science Emergent Data Science Program</a> 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. For details on the event schedule and additional information, please visit: <a href="https://datasciences.utoronto.ca/causal_inference_workshop_2026/" title="https://datasciences.utoronto.ca/causal_inference_workshop_2026/" target="_blank" data-msys-clicktrack="0" rel="nofollow noopener noreferrer">https://datasciences.utoronto.ca/causal_inference_workshop_2026/</a>

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Causal Inference for Networked and Complex Systems

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Causal Inference for Networked and Complex Systems

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