Planning under Distribution Shifts with Causal POMDPs

2026-02-26

AI Summary

This work addresses the challenge that distributional shifts in real-world environments often cause conventional planning models to fail under partial observability. The authors propose a causal POMDP framework that, for the first time, models distributional shifts as causal interventions. Within an augmented belief space, the approach jointly infers latent states and domain changes, enabling policy evaluation and adjustment under hypothetical interventions. Theoretical analysis demonstrates that the value function retains its piecewise-linear and convex structure in this extended space, ensuring the solvability and computational tractability of α-vector–based planning. By actively identifying components of environmental change, the method significantly enhances adaptability in dynamic, non-stationary scenarios.

Technology Category

• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Multiagent Systems: Multiagent Planning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

In the real world, planning is often challenged by distribution shifts. As such, a model of the environment obtained under one set of conditions may no longer remain valid as the distribution of states or the environment dynamics change, which in turn causes previously learned strategies to fail. In this work, we propose a theoretical framework for planning under partial observability using Partially Observable Markov Decision Processes (POMDPs) formulated using causal knowledge. By representing shifts in the environment as interventions on this causal POMDP, the framework enables evaluating plans under hypothesized changes and actively identifying which components of the environment have been altered. We show how to maintain and update a belief over both the latent state and the underlying domain, and we prove that the value function remains piecewise linear and convex (PWLC) in this augmented belief space. Preservation of PWLC under distribution shifts has the advantage of maintaining the tractability of planning via $\alpha$-vector-based POMDP methods.

Problem

• distribution shifts
• planning
• POMDPs
• causal models
• partial observability

Innovation

• Causal POMDP
• Distribution Shift
• Partial Observability
• Piecewise Linear Convex Value Function
• Intervention-based Planning