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.
翻译:如果基础系统将受到积极干预,那么对因果关系的估算就是至关重要的。 建立因果推断引擎的部分原因是确定变量之间的关系,即界定有条件依赖的变量之间的功能关系。 在本文中,我们偏离了因果模型中线性关系的共同假设,即利用神经自动递减密度估测器,并使用它们来估计珍珠号“做量度”框架内的因果影响。我们使用合成数据表明,这种方法可以从非线性系统中获取因果效应,而不必对变量之间的相互作用进行明确的建模。