# Sequential Causal Imitation Learning with Unobserved Confounders

@inproceedings{Kumor2022SequentialCI, title={Sequential Causal Imitation Learning with Unobserved Confounders}, author={Daniel Kumor and Junzhe Zhang}, booktitle={Neural Information Processing Systems}, year={2022} }

“Monkey see monkey do" is an age-old adage, referring to naïve imitation without a deep understanding of a system’s underlying mechanics. Indeed, if a demonstrator has access to information unavailable to the imitator (monkey), such as a different set of sensors, then no matter how perfectly the imitator models its perceived environment (S EE ), attempting to reproduce the demonstrator’s behavior (D O ) can lead to poor outcomes. Imitation learning in the presence of a mismatch between…

## 11 Citations

### Causal Imitation Learning With Unobserved Confounders

- Computer ScienceNeurIPS
- 2020

This paper provides a non-parametric, graphical criterion that is complete (both necessary and sufﬁcient) for determining the feasibility of imitation from the combinations of demonstration data and qualitative assumptions about the underlying environment, represented in the form of a causal model.

### Deconfounded Imitation Learning

- Computer ScienceArXiv
- 2022

This work introduces an algorithm for deconfounded imitation learning, which trains an inference model jointly with a latent-conditional policy and shows in theory and practice that this algorithm converges to the correct interventional policy, solves the confounding issue, and can under certain assumptions achieve an asymptotically optimal imitation performance.

### What Would the Expert do ( · ) ?: Causal Imitation Learning

- Computer Science
- 2021

Modern variants of the classical instrumental variable regression (IVR) technique are applied, enabling us to recover the causally correct underlying policy without requiring access to an interactive expert.

### Sequence Model Imitation Learning with Unobserved Contexts

- Computer ScienceArXiv
- 2022

It is proved that on-policy imitation learning algorithms (with or without access to a queryable expert) are better equipped to handle these sorts of asymptotically realizable problems than off-policy methods.

### Learning Human Driving Behaviors with Sequential Causal Imitation Learning

- Computer ScienceAAAI
- 2022

A sequential causal template is developed that generalizes the default MDP settings to one with Unobserved Confounders (MDPUC-HD) and a sufficient graphical criterion is developed to determine when ignoring causality leads to poor performances in MDPUc-HD.

### A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning

- Computer ScienceICLR
- 2022

It is empirically show that ˆ Z estimated by this method can signiﬁcantly reduce dynamics prediction errors and improve the performance of model-based RL methods on zero-shot new environments with unseen dynamics.

### I NVARIANT C AUSAL R EPRESENTATION L EARNING FOR G ENERALIZATION IN I MITATION AND R EINFORCEMENT L EARNING

- Computer Science
- 2022

A fundamental challenge in imitation and reinforcement learning is to learn policies, representations, or dynamics that do not build on spurious correlations and generalize beyond the specific environments that they were trained on by leveraging a diverse set of training environments.

### Instrumental Variables in Causal Inference and Machine Learning: A Survey

- Computer ScienceArXiv
- 2022

This paper provides the formal formation of IVs and discusses the identiﬁcation problem of IV regression methods under different assumptions, and introduces a variety of applications of IV methods in real-world scenarios and provides a summary of the available datasets and algorithms.

### Adaptively Exploiting d-Separators with Causal Bandits

- Computer ScienceArXiv
- 2022

This work formalize and study the notion of adaptivity, and provides a novel algorithm that simultaneously achieves (a) optimal regret when a d -separator is observed, improving on classical minimax algorithms, and (b) signiﬁcantly smaller regret than recent causal bandit algorithms when the observed variables are not a d-separator.

### Causal Imitation Learning under Temporally Correlated Noise

- Computer ScienceICML
- 2022

Modern variants of the instrumental variable regression (IVR) technique of econometrics are applied, enabling us to recover the underlying policy without requiring access to an interactive expert to break up spurious correlations.

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