New Causal Imitation Learning Method Improves Robustness to Noisy State Observations

Global: New Causal Imitation Learning Method Improves Robustness to Noisy State Observations

Researchers have introduced a novel offline imitation learning framework, named CausIL, that explicitly accounts for measurement error in decision-relevant state variables and aims to maintain policy performance when the data distribution shifts between training and deployment.

Problem Context

In many real‑world applications, part of the state required for decision making is observed only through noisy sensors or indirect measurements. Traditional behavioral cloning (BC) approaches, whether they condition on raw measurements or disregard them, can inadvertently learn spurious state‑action correlations, leading to systematically biased policies under distribution shift.

Causal Framework

The proposed method builds on a causal representation of the relationships among true states, noisy observations, and actions. By treating noisy measurements as proxy variables, CausIL derives a target policy that retains a clear causal interpretation and remains identifiable under specified conditions, even without reward signals or interactive expert queries.

Methodological Details

Identification conditions are formalized to guarantee recoverability of the optimal policy from demonstration data alone. For discrete state spaces, explicit estimators are derived, while continuous settings employ an adversarial learning procedure over reproducing kernel Hilbert space (RKHS) function classes to estimate the necessary parameters.

Experimental Evaluation

The framework was evaluated on semi‑simulated longitudinal data drawn from the PhysioNet/Computing in Cardiology Challenge 2019 cohort. Across multiple distribution‑shift scenarios, CausIL demonstrated superior robustness compared with standard BC baselines, achieving lower policy error rates while preserving performance on the original training distribution.

Implications and Future Directions

By mitigating the impact of noisy state observations, CausIL offers a pathway to more reliable offline learning in domains such as healthcare, robotics, and autonomous systems where measurement error is pervasive. Ongoing work aims to extend the approach to incorporate limited interactive expert feedback and to test scalability on larger, high‑dimensional datasets.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.