Policy Learning And Ope
4concepts
30flashcards
31minutes of reading
- 01 Doubly Robust Estimation Combining an outcome model with a propensity model so that the estimate stays consistent if either one is correct, and why the modern version adds cross-fitting to make that guarantee usable.
- 02 Heterogeneous Treatment Effects and Uplift Estimating who benefits rather than whether the average benefits, and why the target quantity is never observed for any individual, which breaks every standard model-selection habit.
- 03 Inverse Propensity Scoring for Off-Policy Evaluation Estimating how a new policy would have performed using only logs from an old one, by reweighting each logged decision by how much more likely the new policy was to make it.
- 04 Off-Policy Evaluation for Rankers and LLM Systems Applying counterfactual estimation where the action is a ranked list or a generated response, where the action space is effectively unbounded and the logging policy was never stochastic.