DECONFOUNDING REINFORCEMENT LEARNING
Model-Based Reinforcement Learning Under Confounding
Addressing the fundamental inconsistency of model-learning in contextual MDPs where latent variables induce confounding. Our approach combines behavior-averaged transition models with proximal off-policy evaluation.
Executive Impact & Key Findings
Our novel framework offers a principled solution for model learning and planning in complex, real-world environments with unobserved contextual information, leading to more robust and accurate decision-making for enterprise AI.
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Improved Policy Return (vs Naive)
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Reduced Rollout Error (vs Naive)
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Key Assumptions for Identifiability
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Most Model-Based Reinforcement Learning (MBRL) methods assume fully observed data, an assumption often violated in real-world domains. When latent variables (contexts) jointly influence decisions and outcomes, collected data encode arbitrary correlations, leading to confounding. This makes conventional model-learning inconsistent for evaluating state-based policies.
For example, in healthcare, unrecorded doctor's intuition (latent context) affects both treatment choice and patient outcome, creating correlations that misrepresent causal effects. This unobserved confounding is a critical challenge in contextual MDPs, making transition and reward models learned directly from data unreliable for planning.
Our solution reinterprets the Contextual Markov Decision Process (C-MDP) from a causal inference perspective, casting it as a Partially Observed Markov Decision Process (POMDP) where the latent context is an unobserved confounder. We adapt a proximal off-policy evaluation (OPE) technique to identify the confounded reward expectation using observable proxy variables.
This yields a deconfounded Bellman operator, compatible with state-based policies and evaluable from offline data. Integrating this with a behavior-averaged transition model and a maximum causal entropy framework, we construct a Bellman-consistent surrogate MDP. This enables principled model learning and planning in confounded environments without needing to observe the context.
We illustrate our framework using a synthetic clinical decision-making task, modeling a finite-horizon C-MDP for patient treatment. The state space captures severity, and actions represent treatment intensity. The unrecorded, patient-specific context (e.g., comorbidities) acts as a confounder, influencing both physician decisions and outcomes.
Results show that a naive model, using data averaging, accumulates rapid rollout error due to biased transition kernels. In contrast, our proximal learning-based surrogate model consistently achieves smaller L1 error for multi-step rollouts. This demonstrates the enhanced accuracy and robustness of our approach in capturing long-horizon influences of latent risk contexts, even without direct observation.
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References Leveraging Proximal Causal Inference
Enterprise Process Flow
Unobserved Confounding
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Problem as POMDP
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Proximal OPE (Reward Correction)
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Behavior-Averaged Transition
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MaxCausalEnt Framework
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Bellman-Consistent Surrogate MDP
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Principled Model-Based Planning
| Feature | Our Proximal MBRL Approach | Traditional MBRL (Naive) |
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| Contextual Data Handling |
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| Reward Expectation |
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Clinical Treatment Decision Support
Applying Model-Based Reinforcement Learning to optimize treatment decisions in an ICU setting, accounting for unrecorded physician insights (latent context).
Challenge
Doctors' decisions are influenced by unrecorded patient factors (e.g., severity of symptoms, clinical intuition) which confound the observable state-action-reward data. Traditional MBRL would learn biased treatment effects.
Solution
Implemented our proximal MBRL framework to learn a surrogate MDP that deconfounds the reward expectation and transition dynamics. This allows for accurate policy evaluation and planning even with unobserved clinical judgment.
Outcome
Achieved a 1.4% higher expected return compared to a naive learner and significantly reduced multi-step rollout error, demonstrating more reliable and accurate model-based planning for personalized treatment strategies in complex clinical environments.
Advanced ROI Calculator
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Estimated Annual Savings
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Hours Reclaimed Annually
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Implementation Timeline
Our structured approach ensures a smooth transition from current practices to advanced AI-driven decision-making, with clear phases and measurable milestones.
Phase 1: Data Integration & Causal Mapping
Consolidate existing enterprise data, identify potential latent confounders, and map out the causal relationships within your operational processes.
Phase 2: Proxy Variable Identification & Model Training
Utilize existing observable variables as proxies. Train the deconfounded surrogate MDP, adapting to your specific business context and data characteristics.
Phase 3: Policy Optimization & Strategic Planning
Develop and optimize robust AI policies using the learned model. Conduct simulated rollouts to validate performance and inform strategic decision-making.
Phase 4: Deployment & Continuous Learning
Deploy the optimized policies within your enterprise systems. Establish mechanisms for continuous monitoring and model refinement, ensuring sustained performance.
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