DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
Causality has been combined with machine learning to produce robust representations for domain generalization. Most existing methods of this type require massive data from multiple domains to identify causal features by cross-domain variations, which can be expensive or even infeasible and may lead to misidentification in some cases. In this work, we make a different attempt by leveraging the demonstration data distribution to discover the causal features for a domain generalizable policy. We design a novel framework, called DIGIC, to identify the causal features by finding the direct cause of the expert action from the demonstration data distribution via causal discovery. Our framework can achieve domain generalizable imitation learning with only single-domain data and serve as a complement for cross-domain variation-based methods under non-structural assumptions on the underlying causal models. Our empirical study in various control tasks shows that the proposed framework evidently improves the domain generalization performance and has comparable performance to the expert in the original domain simultaneously.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal DiscoveryDomain GeneralizationImitation LearningSimilar Papers 제목 키워드 기반
Causal Discovery and Counterfactual Explanations for Personalized Student Learning
The paper focuses on identifying the causes of student performance to provide personalized recommendations for improving pass rates. We introduce the need to move beyond predictive models and instead identify causal rela…
Causal DiscoveryCausal InferencecounterfactualCausal Reasoning in Pieces: Modular In-Context Learning for Causal Discovery
Causal inference remains a fundamental challenge for large language models. Recent advances in internal reasoning with large language models have sparked interest in whether state-of-the-art reasoning models can robustly…
Causal InferenceCan LLMs Leverage Observational Data? Towards Data-Driven Causal Discovery with LLMs
Causal discovery traditionally relies on statistical methods applied to observational data, often requiring large datasets and assumptions about underlying causal structures. Recent advancements in Large Language Models …
Causal DiscoveryCausal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting
Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making. However, different causal discovery algorithms can produce divergent results that conflict with…
CausalDynamics: A large-scale benchmark for structural discovery of dynamical causal models
Causal discovery for dynamical systems poses a major challenge in fields where active interventions are infeasible. Most methods used to investigate these systems and their associated benchmarks are tailored to determini…
Causal DiscoveryGraph Reconstruction