When Should Reinforcement Learning Use Causal Reasoning?
Authors: Oliver Schulte, Pascal Poupart
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper provides a theoretical study examining which reinforcement learning settings we can expect to benefit from causal reasoning, and how. According to our analysis, the key factor is whether the behavioral policy which generates the data can be executed by the learning agent, meaning that the observation signal available to the learning agent comprises all observations used by the behavioral policy. This paper can therefore serve as a short tutorial on causal modeling for RL researchers. Our analysis follows a ladder of causation as described by (Pearl, 2000): A hierarchy of probabilistic statements that require causal reasoning of increasing complexity. The levels correspond to associations, interventions, and counterfactuals. |
| Researcher Affiliation | Academia | Oliver Schulte EMAIL School of Computing Science, Simon Fraser University, Vancouver, Canada Pascal Poupart EMAIL Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada Vector Institute, Toronto, Canada |
| Pseudocode | No | The paper describes conceptual and theoretical frameworks, definitions, and mathematical equations (e.g., Bellman equations, structural equations), but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement about releasing source code, nor does it provide any links to a code repository. |
| Open Datasets | No | The paper uses illustrative examples (e.g., "simple sports setting," "driving scenario," "frozen lake") to explain theoretical concepts and does not use or refer to any publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | No empirical datasets are used in this theoretical paper, therefore there is no mention of dataset splits. |
| Hardware Specification | No | No empirical experiments were conducted as part of this theoretical study, thus no hardware specifications are provided. |
| Software Dependencies | No | No empirical experiments were conducted that would require specific software dependencies with version numbers. |
| Experiment Setup | No | This paper is a theoretical study and does not describe any specific experimental setup, hyperparameters, or training configurations for empirical evaluation. |