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Causal models bring many benefits to decision-making systems (or agents) bymaking them interpretable, sample-efficient, and robust to changes in the inputdistribution. However, spurious correlations can lead to wrong causal modelsand predictions. We consider the problem of inferring a causal model of areinforcement learning environment and we propose a method to deal withspurious correlations. Specifically, our method designs a reward function thatincentivizes 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 causalmodel. We propose several intervention design methods and compare them. Theexperimental results in a grid-world environment show that our approach leadsto better causal models compared to baselines: learning the model on data froma random policy or a policy trained on the environment's reward. The maincontribution consists of methods to design interventions to resolve spuriouscorrelations.

Resolving Spurious Correlations in Causal Models of Environments via Interventions