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Found 4 papers, 1 papers with code
Augmented balancing weights as linear regression
These popular doubly robust or double machine learning estimators combine outcome modeling with balancing weights -- weights that achieve covariate balance directly in lieu of estimating and inverting the propensity score.
Robust Fitted-Q-Evaluation and Iteration under Sequentially Exogenous Unobserved Confounders
Offline reinforcement learning is important in domains such as medicine, economics, and e-commerce where online experimentation is costly, dangerous or unethical, and where the true model is unknown.
Model-Free and Model-Based Policy Evaluation when Causality is Uncertain
When decision-makers can directly intervene, policy evaluation algorithms give valid causal estimates.
Outcome Assumptions and Duality Theory for Balancing Weights
We study balancing weight estimators, which reweight outcomes from a source population to estimate missing outcomes in a target population.
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