Counterfactual Prediction in Transport Modelling - Ricardo Silva & Charisma Choudhury

Explore counterfactual prediction in transport modeling through this 24-minute workshop from the Alan Turing Institute. Delve into the limitations of traditional prediction algorithms and discover how causal inference can enhance decision-making capabilities. Learn about the outcomes of a Turing Institute challenge focused on addressing methodological challenges in counterfactual prediction. Gain insights into the application of these methods for decision support during the COVID-19 pandemic. Examine topics such as causality in transport modeling, disrupted exits, potential uses of counterfactual prediction, and flow-based featurization. Analyze examples of parameterization, output comparison, and out-of-sample evaluation techniques. Understand the importance of moving beyond expectations and comparing scores in predictive modeling. Conclude with a discussion on exit-count distributions and the broader implications of this research for the field of transport modeling and decision support systems.