Caveats for Causal Reasoning with Equilibrium Models
Caveats for Causal Reasoning with Equilibrium Models
复制标题
使用平衡模型进行因果推理的注意事项
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
Marek J Druzdzel
中科院分区：
文献类型：
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作者：
D. Dash;Marek J Druzdzel
在本文中，我们检查使用递归平衡模型执行因果推理的能力。我们确定了执行因果推理所需的一个关键假设，我们将其称为操纵假设，并且我们证明存在违反操纵假设的一般 F 类递归均衡模型。我们将此类与现有的可逆性现象联系起来，并表明 F 中的所有模型都显示可逆行为，从而为可逆性提供了解释，并表明它是更普遍且可能普遍存在的问题的特例。我们还表明，F 中的所有模型都拥有一组变量 V'，其操纵将导致不稳定，以致系统不存在平衡模型。我们定义了结构稳定性原理，它为因果模型的稳定性提供了图形标准。我们的定理表明，将操纵假设应用于平衡模型时可能会得到完全错误的推论，这一结果对当前因果建模工作，特别是从数据中发现因果关系具有影响。
In this paper we examine the ability to perform causal reasoning with recursive equilibrium models. We identify a critical postulate, which we term the Manipulation Postulate, that is required in order to perform causal inference, and we prove that there exists a general class F of recursive equilibrium models that violate the Manipulation Postulate. We relate this class to the existing phenomenon of reversibility and show that all models in F display reversible behavior, thereby providing an explanation for reversibility and suggesting that it is a special case of a more general and perhaps widespread problem. We also show that all models in F possess a set of variables V′ whose manipulation will cause an instability such that no equilibrium model will exist for the system. We define the Structural Stability Principle which provides a graphical criterion for stability in causal models. Our theorems suggest that drastically incorrect inferences may be obtained when applying the Manipulation Postulate to equilibrium models, a result which has implications for current work on causal modeling, especially causal discovery from data.