#ope

6 notes

• Anytime-Valid OPE Anytime-valid off-policy evaluation that provides time-uniform off-policy value confidence sequences valid at any stopping time; based on e-processes/confidence sequences.
• MDP (Markov Decision Process) A Markov Decision Process (MDP) is a mathematical framework for sequential decision-making problems.
• Off-Policy Evaluation (OPE) Estimate the value $V(\pie)=E{\pie}[\sum r]$ of a target policy $\pie$ from logs collected under a different behavior policy $\pib$.
• OPE Deployment-Gate Playbook — Ruling "Trust / Distrust / A/B" Without Ground Truth Operating rules for an OPE deployment gate that rules on deployment from recommendation logs alone, under the real-world condition that nobody knows the true value V(π_e) — a 3-way gate over three diagnostics, the validity battery (a necessary-condition screen), the Λ-sensitivity certificate, the comparative-first principle, and the in-principle limit called observational equivalence. A playbook curation of the ope-to-decision benchmark.
• Policy Trees Policy Trees, proposed by Athey & Wager (2021), are an interpretable policy-learning method.
• RTB Bidding Strategy via Causal ML — From Prediction to Optimization A five-stage case study on the public iPinYou RTB dataset that moves from pCTR/pCVR prediction through causal effect estimation (CATE, SCM) to budget-constrained optimal bidding and off-policy policy evaluation.