Causal discovery with endogenous context variables
具有内生情境变量的因果发现AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究内生情境下的约束式因果发现、结构因果模型与因果解释条件。
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具有内生情境变量的因果发现
- 研究问题
- 当决定因果机制变化的情境变量本身也受系统变量影响时，如何发现情境特定的因果关系并避免分组分析误导？
- 方法与思路
- 作者讨论基于约束的因果发现假设，提出自适应算法，并结合结构因果模型和充分性假设论证结果的正确性。
- 材料陈述的贡献
- 摘要称朴素分情境或合并数据的方法可能缺乏信息，提出算法及其因果解释条件，并以实验展示表现和当前限制。
- 阅读关注点
- 应核对内生情境的建模方式、条件独立信息的使用与充分性假设，重点查看算法正确性范围及实验揭示的失败情形。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未列明完整假设和限制，无法判断有限样本或不同反馈结构中的可靠性。
原文摘要
Causal systems often exhibit variations of the underlying causal mechanisms between the variables of the system. Often, these changes are driven by different environments or internal states in which the system operates, and we refer to context variables as those variables that indicate this change in causal mechanisms. An example are the causal relations in soil moisture-temperature interactions and their dependence on soil moisture regimes: Dry soil triggers a dependence of soil moisture on latent heat, while environments with wet soil do not feature such a feedback, making it a context-specific property. Crucially, a regime or context variable such as soil moisture need not be exogenous and can be influenced by the dynamical system variables - precipitation can make a dry soil wet - leading to joint systems with endogenous context variables. In this work we investigate the assumptions for constraint-based causal discovery of context-specific information in systems with endogenous context variables. We show that naive approaches such as learning different regime graphs on masked data, or pooling all data, can lead to uninformative results. We propose an adaptive constraint-based discovery algorithm and give a detailed discussion on the connection to structural causal models, including sufficiency assumptions, which allow to prove the soundness of our algorithm and to interpret the results causally. Numerical experiments demonstrate the performance of the proposed method over alternative baselines, but they also unveil current limitations of our method.