论文标题
通过神经自回归密度估计器估计因果效应
Estimating Causal Effects with the Neural Autoregressive Density Estimator
论文作者
论文摘要
在情况下，因果效应的估计是基本系统将受到主动干预措施的基础。构建因果推理引擎的一部分是定义变量如何相互关系，即定义给定条件依赖性变量之间的功能关系。在本文中，我们通过利用神经自回归密度估计器来偏离因果模型中线性关系的共同假设，并利用它们来估计Pearl的Do-Calculus框架内的因果关系。使用合成数据，我们表明该方法可以从非线性系统中检索因果关系的因果影响，而无需明确建模变量之间的相互作用。
Estimation of causal effects is fundamental in situations were the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional relationship between variables given conditional dependencies. In this paper, we deviate from the common assumption of linear relationships in causal models by making use of neural autoregressive density estimators and use them to estimate causal effects within the Pearl's do-calculus framework. Using synthetic data, we show that the approach can retrieve causal effects from non-linear systems without explicitly modeling the interactions between the variables.
