We extend the definition of the marginal causal effect to the continuous treatment setting and develop a novel characterization of causal bias in the framework of structural causal models. We prove that our derived bias expression is zero if, and only if, the causal effect is identifiable via covariate adjustment. We show that under some restrictions on the structural equations, the causal bias can be estimated efficiently and allows for causal regularization of predictive probabilistic models. We demonstrate the effectiveness of our method for causal bias quantification in various settings where (not) controlling for certain covariates would introduce causal bias.
翻译:我们把边际因果关系的定义扩大到持续治疗环境,并在结构性因果模型框架内对因果偏见进行新的定性。我们证明,如果,而且只有在通过共变调整可以确定因果影响的情况下,我们产生的偏见表达是零的。我们表明,在对结构方程式的某些限制下,可以有效地估计因果偏见,并允许预测性概率模型的因果规范化。我们证明,在(不)控制某些共变因素会导致因果偏见的各种环境中,我们为因果偏见量化的方法是有效的。