DODO通过有限干预自主学习因果结构，提升复杂环境下的推理能力。
DODO: Causal Structure Learning with Budgeted Interventions
- 基于有限干预的主动学习策略，逐步构建因果图
- 在噪声环境下实现接近零错误的因果结构重建
- 适合资源受限但需高可靠性因果推断的应用场景
近年来，人工智能在识别复杂相关性方面取得显著进展，但其性能仍依赖于表层关联。赋予AI因果意识可增强对环境机制的深层理解。本文提出DODO算法，使智能体通过重复干预自主学习环境中的因果结构。假设环境遵循一个隐藏的因果有向无环图（DAG），智能体需在存在噪声的情况下准确推断该结构。通过实施干预并利用因果推断技术分析观测变化的统计显著性，DODO在几乎所有非极端资源条件下均优于纯观察方法。实验表明，DODO可实现接近零错误的因果图重建；在最挑战性的配置下，其F1分数比最佳基线高出0.25点。
原文摘要 · Abstract (English)
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.
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