Enriching Latent Class Models With Counterfactual Prediction - Mark Gilthorpe

Explore a 21-minute workshop from the Alan Turing Institute on enriching latent class models with counterfactual prediction. Delve into the limitations of traditional prediction algorithms in AI and discover how causal inference can enhance their capabilities for 'what if' scenarios. Learn about the outcomes of a Turing Institute challenge, focusing on methodological approaches to counterfactual prediction. Gain insights into practical applications, including decision support for the COVID-19 pandemic. Cover topics such as risk prediction factors, conceptual views, models, examples, results, assumptions, and future work in this field.

Introduction
Factors which affect risk prediction
Conceptual view
Models
Example
Results
Examples
Assumptions
Future work