The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence) Applying the Causal Roadmap to Optimal Dynamic Treatment Rules with Lina Montoya - #506
Aug 2, 2021
Join Lina Montoya, a postdoctoral researcher at UNC Chapel Hill focused on causal inference in precision medicine. She dives into her innovative work on Optimal Dynamic Treatment rules, particularly in the U.S. criminal justice system. Lina discusses the critical role of neglected assumptions in causal inference, the super learner algorithm's impact on predicting treatment effectiveness, and future research directions aimed at optimizing therapy delivery in resource-constrained settings like rural Kenya. This engaging discussion highlights the intersection of AI, healthcare, and justice.
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ODT and Causal Inference
- Optimal Dynamic Treatment (ODT) rules are crucial for causal inference.
- The assumptions needed to estimate ODT's causal parameters are unique to the specific question being asked.
Causal Roadmap
- The causal roadmap provides a structured approach to causal questions.
- It helps determine if data and tools are sufficient to answer the question.
Causal vs. Statistical Parameters
- Causal parameters, based on counterfactuals, represent ideal scenarios.
- Statistical parameters use observable data.