#doubly-robust

7 notes

• AIPW (Augmented Inverse Probability Weighting) - $\hat{\mu}_t(X)$: Outcome model ($E[Y|T=t, X]$)
• Double/Debiased Machine Learning (DML) A methodology for performing valid statistical inference on a low-dimensional parameter of interest $\theta0$ in the presence of a high-dimensional nuisance parameter $\eta0$.
• Doubly Robust Estimator The Doubly Robust (DR) Estimator combines an outcome-regression model and a propensity-score model, remaining consistent as long as just one of the two is correctly specified.
• DR-Learner The DR-Learner is a two-stage doubly robust estimator for CATE that regresses a pseudo-outcome on the covariates.
• ESCM² (Entire Space Counterfactual Multi-Task Model) A model that integrates a counterfactual risk regularizer based on the Inverse Propensity Score (IPS) and the Doubly Robust estimator into ESMM, in order to address ESMM's two theoretical limitations — Inherent Estimation Bias (IEB) and Potential Independence Priority (PIP).
• 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 $\pib$.
• Optimal Targeting Policy An Optimal Targeting Policy maps covariates $x$ to a treatment decision $\pi(x)\in\{0,1\}$ so as to maximize policy value: