DODO: Causal Structure Learning with Budgeted Interventions
This work addresses the challenge of enabling causality awareness in AI for agents interacting with complex environments, representing an incremental improvement over existing methods.
The paper tackles the problem of learning causal structures from environments with hidden directed acyclic graphs by introducing DODO, an algorithm that uses interventions to infer these structures, achieving up to zero errors in reconstruction and outperforming baselines by +0.25 F1 points in challenging cases.
Artificial Intelligence has achieved remarkable advancements in recent years, yet much of its progress relies on identifying increasingly complex correlations. Enabling causality awareness in AI has the potential to enhance its performance by enabling a deeper understanding of the underlying mechanisms of the environment. In this paper, we introduce DODO, an algorithm defining how an Agent can autonomously learn the causal structure of its environment through repeated interventions. We assume a scenario where an Agent interacts with a world governed by a causal Directed Acyclic Graph (DAG), which dictates the system's dynamics but remains hidden from the Agent. The Agent's task is to accurately infer the causal DAG, even in the presence of noise. To achieve this, the Agent performs interventions, leveraging causal inference techniques to analyze the statistical significance of observed changes. Results show better performance for DODO, compared to observational approaches, in all but the most limited resource conditions. DODO is often able to reconstruct with as low as zero errors the structure of the causal graph. In the most challenging configuration, DODO outperforms the best baseline by +0.25 F1 points.