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Causal interpretation of stochastic differential equations

机译:随机微分方程的因果解释

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We give a causal interpretation of stochastic differential equations (SDEs) by defining the postintervention SDE resulting from an intervention in an SDE. We show that under Lipschitz conditions, the solution to the postintervention SDE is equal to a uniform limit in probability of postintervention structural equation models based on the Euler scheme of the original SDE, thus relating our definition to mainstream causal concepts. We prove that when the driving noise in the SDE is a Lévy process, the postintervention distribution is identifiable from the generator of the SDE.
机译:通过定义干预后的SDE,我们定义了干预后的SDE,从而给出了随机微分方程(SDE)的因果解释。我们表明,在Lipschitz条件下,干预后SDE的解等于基于原始SDE的Euler方案的干预后结构方程模型概率的统一极限,从而将我们的定义与主流因果关系联系起来。我们证明,当SDE中的驱动噪声是一个Lévy过程时,可以从SDE的生成器中识别干预后的分布。

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