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Found 17 papers, 5 papers with code
Reward-Relevance-Filtered Linear Offline Reinforcement Learning
This paper studies offline reinforcement learning with linear function approximation in a setting with decision-theoretic, but not estimation sparsity.
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.
A Note on Task-Aware Loss via Reweighing Prediction Loss by Decision-Regret
In this short technical note we propose a baseline for decision-aware learning for contextual linear optimization, which solves stochastic linear optimization when cost coefficients can be predicted based on context information.
Data-Driven Influence Functions for Optimization-Based Causal Inference
We study a constructive algorithm that approximates Gateaux derivatives for statistical functionals by finite differencing, with a focus on functionals that arise in causal inference.
Off-Policy Evaluation with Policy-Dependent Optimization Response
Under this framework, a decision-maker's utility depends on the policy-dependent optimization, which introduces a fundamental challenge of \textit{optimization} bias even for the case of policy evaluation.
Stateful Offline Contextual Policy Evaluation and Learning
We study off-policy evaluation and learning from sequential data in a structured class of Markov decision processes that arise from repeated interactions with an exogenous sequence of arrivals with contexts, which generate unknown individual-level responses to agent actions.
Fairness, Welfare, and Equity in Personalized Pricing
These different application areas may lead to different concerns around fairness, welfare, and equity on different objectives: price burdens on consumers, price envy, firm revenue, access to a good, equal access, and distributional consequences when the good in question further impacts downstream outcomes of interest.
Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning
We develop a robust approach that estimates sharp bounds on the (unidentifiable) value of a given policy in an infinite-horizon problem given data from another policy with unobserved confounding, subject to a sensitivity model.
Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit.
Assessing Disparate Impacts of Personalized Interventions: Identifiability and Bounds
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit.
Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination
In this paper we study a fundamental challenge to assessing disparate impacts in practice: protected class membership is often not observed in the data.
The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the xAUC Metric
To better account for this, in this paper, we investigate the fairness of predictive risk scores from the point of view of a bipartite ranking task, where one seeks to rank positive examples higher than negative ones.
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding
We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders.
Residual Unfairness in Fair Machine Learning from Prejudiced Data
We connect these lines of work and study the residual unfairness that arises when a fairness-adjusted predictor is not actually fair on the target population due to systematic censoring of training data by existing biased policies.
Confounding-Robust Policy Improvement
We study the problem of learning personalized decision policies from observational data while accounting for possible unobserved confounding.
Policy Evaluation and Optimization with Continuous Treatments
We study the problem of policy evaluation and learning from batched contextual bandit data when treatments are continuous, going beyond previous work on discrete treatments.
Dynamic Task Allocation for Crowdsourcing Settings
We consider the problem of optimal budget allocation for crowdsourcing problems, allocating users to tasks to maximize our final confidence in the crowdsourced answers.
