Applying the Causal Roadmap to Optimal Dynamic Treatment Rules with Lina Montoya - #506

This article summarizes a podcast episode from Practical AI featuring Lina Montoya, a postdoctoral researcher. The episode focuses on Montoya's research applying Optimal Dynamic Treatment (ODT) to the US criminal justice system. The discussion covers neglected assumptions in causal inference, the causal roadmap developed at UC Berkeley, and how Montoya uses a "superlearner" algorithm to estimate ODT rules. The article highlights the application of advanced AI techniques to real-world problems and the importance of understanding causal relationships for effective interventions.

• •The podcast episode explores the application of Optimal Dynamic Treatment (ODT) in the criminal justice system.
• •It discusses the causal roadmap and its connection to ODT rule estimation.
• •The research utilizes a "superlearner" algorithm for training and analysis.