# Resolving Spurious Correlations in Causal Models of Environments via Interventions

arXiv: https://arxiv.org/abs/2002.05217 · HTML: https://mlsanity.com/paper/2002.05217/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2020.

## Built on by (most cited first, 14 of 14)

- [Causal Dynamics Learning for Task-Independent State Abstraction](https://mlsanity.com/paper/2206.13452/) (2022)
- [A Survey on Causal Reinforcement Learning](https://mlsanity.com/paper/2302.05209/) (2023)
- [Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning](https://mlsanity.com/paper/2207.09081/) (2022)
- [The Essential Role of Causality in Foundation World Models for Embodied AI](https://mlsanity.com/paper/2402.06665/) (2024)
- [A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement Learning](https://mlsanity.com/paper/2206.04551/) (2022)
- [Invariant Causal Imitation Learning for Generalizable Policies](https://mlsanity.com/paper/2311.01489/) (2023)
- [Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement Learning](https://mlsanity.com/paper/2406.03234/) (2024)
- [Causal Analysis of Agent Behavior for AI Safety](https://mlsanity.com/paper/2103.03938/) (2021)
- [BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning](https://mlsanity.com/paper/2407.10967/) (2024)
- [Causal prompting model-based offline reinforcement learning](https://mlsanity.com/paper/2406.01065/) (2024)
- [Efficient and Generalizable Environmental Understanding for Visual Navigation](https://mlsanity.com/paper/2506.15377/) (2025)
- [To do or not to do: finding causal relations in smart homes](https://mlsanity.com/paper/2105.10058/) (2021)
- [The Pragmatic Frames of Spurious Correlations in Machine Learning: Interpreting How and Why They Matter](https://mlsanity.com/paper/2411.04696/) (2024)
- [Parameter Estimation using Reinforcement Learning Causal Curiosity: Limits and Challenges](https://mlsanity.com/paper/2505.08453/) (2025)

## Abstract (arXiv)

Causal models bring many benefits to decision-making systems (or agents) by making them interpretable, sample-efficient, and robust to changes in the input distribution. However, spurious correlations can lead to wrong causal models and predictions. We consider the problem of inferring a causal model of a reinforcement learning environment and we propose a method to deal with spurious correlations. Specifically, our method designs a reward function that incentivizes an agent to do an intervention to find errors in the causal model. The data obtained from doing the intervention is used to improve the causal model. We propose several intervention design methods and compare them. The experimental results in a grid-world environment show that our approach leads to better causal models compared to baselines: learning the model on data from a random policy or a policy trained on the environment's reward. The main contribution consists of methods to design interventions to resolve spurious correlations.