mlsanityData to 14 Oct 2026

Causal Imitation Learning Under Measurement Error and Distribution Shift

arXiv 2601.22206 ↗First posted 29 Jan 2026cs.LG stat.ME stat.MLcited by 0

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

We study offline imitation learning (IL) when part of the decision-relevant state is observed only through noisy measurements and the distribution may change between training and deployment. Such settings induce spurious state-action correlations, so standard behavioral cloning (BC) -- whether conditioning on raw measurements or ignoring them -- can converge to systematically biased policies under distribution shift. We propose a general framework for IL under measurement error, inspired by explicitly modeling the causal relationships among the variables, yielding a target that retains a causal interpretation and is robust to distribution shift. Building on ideas from proximal causal inference, we introduce textttCausIL, which treats noisy state observations as proxy variables, and we provide identification conditions under which the target policy is recoverable from demonstrations without rewards or interactive expert queries. We develop estimators for both discrete and continuous state spaces; for continuous settings, we use an adversarial procedure over RKHS function classes to learn the required parameters. We evaluate textttCausIL on semi-simulated longitudinal data from the PhysioNet/Computing in Cardiology Challenge 2019 cohort and demonstrate improved robustness to distribution shift compared to BC baselines.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 22 of its 34 references that are in the map. First posted on arXiv 29 Jan 2026.

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22 of 34 references.

Closest ancestors · 9

Same direction, newest first

  1. 2023Initial State Interventions for Deconfounded Imitation Learning29 Jul
  2. 2022Sequential Causal Imitation Learning with Unobserved Confounders12 Aug
  3. 2022Causal Imitation Learning with Unobserved Confounders12 Aug
  4. 2022Sequence Model Imitation Learning with Unobserved Contexts3 Aug
  5. 2022Causal Imitation Learning under Temporally Correlated Noise2 Feb
  6. 2021Of Moments and Matching: A Game-Theoretic Framework for Closing the Imitation Gap4 Mar
  7. 2021Feedback in Imitation Learning: The Three Regimes of Covariate Shift4 Feb
  8. 2020Fighting Copycat Agents in Behavioral Cloning from Observation Histories28 Oct
  9. 2019Causal Confusion in Imitation Learning28 May

Other references · 13

  1. 2016Generative Adversarial Imitation Learningcited by 1,597
  2. 2016Identifying Causal Effects With Proxy Variables of an Unmeasured Confoundercited by 139
  3. 2014Reinforcement and Imitation Learning via Interactive No-Regret Learningcited by 118
  4. 2020An Introduction to Proximal Causal Learningcited by 95
  5. 2020Minimax Estimation of Conditional Moment Modelscited by 83
  6. 2019Off-Policy Evaluation in Partially Observable Environmentscited by 66
  7. 2021Shaking the foundations: delusions in sequence models for interaction and controlcited by 62
  8. 2021Proximal Reinforcement Learning: Efficient Off-Policy Evaluation in Partially Observed Markov Decision Processescited by 41
  9. 2021A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processescited by 36
  10. 2022Off-Policy Confidence Interval Estimation with Confounded Markov Decision Processcited by 31
  11. 2021Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inferencecited by 20
  12. 2022Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Modelscited by 18
1 more
  1. 2021Proximal Causal Inference for Complex Longitudinal Studiescited by 16

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  1. 2025Confounded Causal Imitation Learning with Instrumental Variables
  2. 2026Scalable Causal Imitation Learning
  3. 2021Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage
  4. 2025Causal Imitation Learning under Expert-Observable and Expert-Unobservable Confounding
  5. 2023Offline Imitation Learning with Suboptimal Demonstrations via Relaxed Distribution Matching
  6. 2024Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
  7. 2021A Critique of Strictly Batch Imitation Learning
  8. 2022Reliable Conditioning of Behavioral Cloning for Offline Reinforcement Learning