New Study Introduces Causal Imitation Learning Framework for Hidden Confounders

Global: New Study Introduces Causal Imitation Learning Framework for Hidden Confounders

A team of researchers—including Daqian Shao, Thomas Kleine Buening, and Marta Kwiatkowska—published a paper on February 11, 2025, with a revised version released on January 29, 2026, that proposes a general framework for causal imitation learning (IL) when hidden confounders are present. The work appears on the preprint server arXiv and addresses the challenge of learning policies from expert demonstrations that may be influenced by variables unseen by the learner.

Framework Extends Existing Settings

The authors distinguish two categories of hidden confounders: (a) variables observable to the expert but not to the imitator, and (b) confounding noise that is hidden from both parties. By explicitly modeling these scenarios, the framework subsumes several previously studied causal IL configurations.

Instrumental Variable Reformulation

To handle the unobserved influences, the paper leverages trajectory histories as instrumental variables. This approach recasts the causal IL problem as a Conditional Moment Restriction (CMR) task, enabling the application of established econometric techniques within the imitation‑learning context.

Algorithmic Solution: DML‑IL

The study introduces DML‑IL, an algorithm that solves the CMR formulation via double‑machine‑learning (DML) instrumental‑variable regression. The authors provide a theoretical upper bound on the imitation gap, quantifying how closely the learned policy can approximate the expert’s behavior under the defined confounding assumptions.

Empirical Evaluation

Experimental results focus on continuous state‑action environments, including benchmark Mujoco tasks. According to the authors, DML‑IL consistently outperforms existing causal IL baselines across these domains, demonstrating lower imitation error and improved stability.

Implications for Research

The findings suggest that treating trajectory histories as instruments can broaden the applicability of causal IL methods to settings where experts have access to privileged information. Consequently, the approach may inform future studies that aim to bridge gaps between observational data and actionable policies.

Future Directions

The authors note that extending the framework to high‑dimensional observation spaces and exploring alternative instrument constructions are promising avenues for further investigation. They also acknowledge that real‑world validation beyond simulated environments remains an open challenge.

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.