Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions

• URL: http://arxiv.org/abs/2002.03478v3
• Date: Tue, 11 Aug 2020 06:51:45 GMT
• Title: Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions
• Authors: Omer Gottesman, Joseph Futoma, Yao Liu, Sonali Parbhoo, Leo Anthony Celi, Emma Brunskill, Finale Doshi-Velez
• Abstract summary: Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education. Traditional measures such as confidence intervals may be insufficient due to noise, limited data and confounding. We develop a method that could serve as a hybrid human-AI system, to enable human experts to analyze the validity of policy evaluation estimates.
• Score: 48.91284724066349
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education, but safe deployment in high stakes settings requires ways of assessing its validity. Traditional measures such as confidence intervals may be insufficient due to noise, limited data and confounding. In this paper we develop a method that could serve as a hybrid human-AI system, to enable human experts to analyze the validity of policy evaluation estimates. This is accomplished by highlighting observations in the data whose removal will have a large effect on the OPE estimate, and formulating a set of rules for choosing which ones to present to domain experts for validation. We develop methods to compute exactly the influence functions for fitted Q-evaluation with two different function classes: kernel-based and linear least squares, as well as importance sampling methods. Experiments on medical simulations and real-world intensive care unit data demonstrate that our method can be used to identify limitations in the evaluation process and make evaluation more robust.

Related papers

• Targeted Machine Learning for Average Causal Effect Estimation Using the Front-Door Functional [3.0232957374216953]
  evaluating the average causal effect (ACE) of a treatment on an outcome often involves overcoming the challenges posed by confounding factors in observational studies. Here, we introduce novel estimation strategies for the front-door criterion based on the targeted minimum loss-based estimation theory. We demonstrate the applicability of these estimators to analyze the effect of early stage academic performance on future yearly income.
  arXiv  Detail & Related papers  (2023-12-15T22:04:53Z)
• Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classification [3.7305040207339286]
  We develop Conformal Prediction, Monte Carlo Dropout, and Evidential Deep Learning approaches to assess uncertainty quantification in deep neural networks. Results: The experimental results demonstrate a significant enhancement in uncertainty quantification with the utilization of the Conformal Prediction method. Our conclusion highlights a robust and consistent performance of conformal prediction across diverse testing conditions.
  arXiv  Detail & Related papers  (2023-12-12T17:37:16Z)
• B-Learner: Quasi-Oracle Bounds on Heterogeneous Causal Effects Under Hidden Confounding [51.74479522965712]
  We propose a meta-learner called the B-Learner, which can efficiently learn sharp bounds on the CATE function under limits on hidden confounding. We prove its estimates are valid, sharp, efficient, and have a quasi-oracle property with respect to the constituent estimators under more general conditions than existing methods.
  arXiv  Detail & Related papers  (2023-04-20T18:07:19Z)
• Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processes [65.91730154730905]
  In applications of offline reinforcement learning to observational data, such as in healthcare or education, a general concern is that observed actions might be affected by unobserved factors. Here we tackle this by considering off-policy evaluation in a partially observed Markov decision process (POMDP) We extend the framework of proximal causal inference to our POMDP setting, providing a variety of settings where identification is made possible.
  arXiv  Detail & Related papers  (2021-10-28T17:46:14Z)
• Evaluating the Robustness of Off-Policy Evaluation [10.760026478889664]
  Off-policy Evaluation (OPE) evaluates the performance of hypothetical policies leveraging only offline log data. It is particularly useful in applications where the online interaction involves high stakes and expensive setting. We develop Interpretable Evaluation for Offline Evaluation (IEOE), an experimental procedure to evaluate OPE estimators' robustness.
  arXiv  Detail & Related papers  (2021-08-31T09:33:13Z)
• Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation [53.83642844626703]
  We provide a unifying framework for estimating higher-order derivatives of value functions, based on off-policy evaluation. Our framework interprets a number of prior approaches as special cases and elucidates the bias and variance trade-off of Hessian estimates.
  arXiv  Detail & Related papers  (2021-06-24T15:58:01Z)
• Estimating and Improving Fairness with Adversarial Learning [65.99330614802388]
  We propose an adversarial multi-task training strategy to simultaneously mitigate and detect bias in the deep learning-based medical image analysis system. Specifically, we propose to add a discrimination module against bias and a critical module that predicts unfairness within the base classification model. We evaluate our framework on a large-scale public-available skin lesion dataset.
  arXiv  Detail & Related papers  (2021-03-07T03:10:32Z)
• Bootstrapping Statistical Inference for Off-Policy Evaluation [43.79456564713911]
  We study the use of bootstrapping in off-policy evaluation (OPE) We propose a bootstrapping FQE method for inferring the distribution of the policy evaluation error and show that this method is efficient and consistent for off-policy statistical inference. We evaluate the bootrapping method in classical RL environments for confidence interval estimation, estimating the variance of off-policy evaluator, and estimating the correlation between multiple off-policy evaluators.
  arXiv  Detail & Related papers  (2021-02-06T16:45:33Z)
• Reliable Off-policy Evaluation for Reinforcement Learning [53.486680020852724]
  In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy. We propose a novel framework that provides robust and optimistic cumulative reward estimates using one or multiple logged data.
  arXiv  Detail & Related papers  (2020-11-08T23:16:19Z)
• Accountable Off-Policy Evaluation With Kernel Bellman Statistics [29.14119984573459]
  We consider off-policy evaluation (OPE), which evaluates the performance of a new policy from observed data collected from previous experiments. Due to the limited information from off-policy data, it is highly desirable to construct rigorous confidence intervals, not just point estimation. We propose a new variational framework which reduces the problem of calculating tight confidence bounds in OPE.
  arXiv  Detail & Related papers  (2020-08-15T07:24:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.