Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences

• URL: http://arxiv.org/abs/2407.17910v1
• Date: Thu, 25 Jul 2024 10:02:11 GMT
• Title: Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal Interferences
• Authors: Runpeng Dai, Jianing Wang, Fan Zhou, Shikai Luo, Zhiwei Qin, Chengchun Shi, Hongtu Zhu,
• Abstract summary: Offcommerce evaluation (OPE) is widely applied in sectors such as pharmaceuticals and e-policy-policy. This paper introduces a causal deepset framework that relaxes several key structural assumptions. We present novel algorithms that incorporate the PI assumption into OPE and thoroughly examine their theoretical foundations.
• Score: 24.361550505778155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Off-policy evaluation (OPE) is widely applied in sectors such as pharmaceuticals and e-commerce to evaluate the efficacy of novel products or policies from offline datasets. This paper introduces a causal deepset framework that relaxes several key structural assumptions, primarily the mean-field assumption, prevalent in existing OPE methodologies that handle spatio-temporal interference. These traditional assumptions frequently prove inadequate in real-world settings, thereby restricting the capability of current OPE methods to effectively address complex interference effects. In response, we advocate for the implementation of the permutation invariance (PI) assumption. This innovative approach enables the data-driven, adaptive learning of the mean-field function, offering a more flexible estimation method beyond conventional averaging. Furthermore, we present novel algorithms that incorporate the PI assumption into OPE and thoroughly examine their theoretical foundations. Our numerical analyses demonstrate that this novel approach yields significantly more precise estimations than existing baseline algorithms, thereby substantially improving the practical applicability and effectiveness of OPE methodologies. A Python implementation of our proposed method is available at https://github.com/BIG-S2/Causal-Deepsets.

Related papers

• Bayesian Off-Policy Evaluation and Learning for Large Action Spaces [14.203316003782604]
  In interactive systems, actions are often correlated, presenting an opportunity for more sample-efficient off-policy evaluation and learning. We introduce a unified Bayesian framework to capture these correlations through structured and informative priors. We propose sDM, a generic Bayesian approach for OPE and OPL, grounded in both algorithmic and theoretical foundations.
  arXiv  Detail & Related papers  (2024-02-22T16:09:45Z)
• Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-Constraint [56.74058752955209]
  This paper studies the alignment process of generative models with Reinforcement Learning from Human Feedback (RLHF) We first identify the primary challenges of existing popular methods like offline PPO and offline DPO as lacking in strategical exploration of the environment. We propose efficient algorithms with finite-sample theoretical guarantees.
  arXiv  Detail & Related papers  (2023-12-18T18:58:42Z)
• Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation [137.3520153445413]
  A notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. We evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes.
  arXiv  Detail & Related papers  (2023-07-11T02:58:10Z)
• Uncertainty-Aware Instance Reweighting for Off-Policy Learning [63.31923483172859]
  We propose a Uncertainty-aware Inverse Propensity Score estimator (UIPS) for improved off-policy learning. Experiment results on synthetic and three real-world recommendation datasets demonstrate the advantageous sample efficiency of the proposed UIPS estimator.
  arXiv  Detail & Related papers  (2023-03-11T11:42:26Z)
• Off-policy evaluation for learning-to-rank via interpolating the item-position model and the position-based model [83.83064559894989]
  A critical need for industrial recommender systems is the ability to evaluate recommendation policies offline, before deploying them to production. We develop a new estimator that mitigates the problems of the two most popular off-policy estimators for rankings. In particular, the new estimator, called INTERPOL, addresses the bias of a potentially misspecified position-based model.
  arXiv  Detail & Related papers  (2022-10-15T17:22:30Z)
• Offline Policy Optimization with Eligible Actions [34.4530766779594]
  offline policy optimization could have a large impact on many real-world decision-making problems. Importance sampling and its variants are a commonly used type of estimator in offline policy evaluation. We propose an algorithm to avoid this overfitting through a new per-state-neighborhood normalization constraint.
  arXiv  Detail & Related papers  (2022-07-01T19:18:15Z)
• Off-Policy Evaluation for Large Action Spaces via Embeddings [36.42838320396534]
  Off-policy evaluation (OPE) in contextual bandits has seen rapid adoption in real-world systems. Existing OPE estimators degrade severely when the number of actions is large. We propose a new OPE estimator that leverages marginalized importance weights when action embeddings provide structure in the action space.
  arXiv  Detail & Related papers  (2022-02-13T14:00:09Z)
• Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning [8.318053414415903]
  This paper aims to overcome difficulties in policy evaluation for online learning. We derive the probability of exploration that quantifies the probability of exploring the non-optimal actions. We develop the doubly robust interval estimation (DREAM) method to infer the value under the estimated optimal policy.
  arXiv  Detail & Related papers  (2021-10-29T02:38:54Z)
• COMBO: Conservative Offline Model-Based Policy Optimization [120.55713363569845]
  Uncertainty estimation with complex models, such as deep neural networks, can be difficult and unreliable. We develop a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-actions. We find that COMBO consistently performs as well or better as compared to prior offline model-free and model-based methods.
  arXiv  Detail & Related papers  (2021-02-16T18:50:32Z)
• Discrete Action On-Policy Learning with Action-Value Critic [72.20609919995086]
  Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension. We construct a critic to estimate action-value functions, apply it on correlated actions, and combine these critic estimated action values to control the variance of gradient estimation. These efforts result in a new discrete action on-policy RL algorithm that empirically outperforms related on-policy algorithms relying on variance control techniques.
  arXiv  Detail & Related papers  (2020-02-10T04:23:09Z)

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.