论文标题
深层因果图的因果推断
Causal Inference with Deep Causal Graphs
论文作者
论文摘要
参数因果建模技术很少为反事实估计提供功能，通常是以建模复杂性为代价。由于因果估计取决于用于建模数据的功能家族，因此简单的模型可能需要不精确地表征生成机制，因此结果不可靠。这将其适用性限制在现实生活数据集中，并具有非线性关系和变量之间的高相互作用。我们提出了深层因果图，这是神经网络所需功能的抽象规范，以建模因果分布，并提供满足该合同的模型：正常的因果流量。我们在建模复杂的相互作用并展示了使用真正的因果反事实来建模的复杂交互作用和展示该方法的应用解释性和公平性的应用中的表现力。
Parametric causal modelling techniques rarely provide functionality for counterfactual estimation, often at the expense of modelling complexity. Since causal estimations depend on the family of functions used to model the data, simplistic models could entail imprecise characterizations of the generative mechanism, and, consequently, unreliable results. This limits their applicability to real-life datasets, with non-linear relationships and high interaction between variables. We propose Deep Causal Graphs, an abstract specification of the required functionality for a neural network to model causal distributions, and provide a model that satisfies this contract: Normalizing Causal Flows. We demonstrate its expressive power in modelling complex interactions and showcase applications of the method to machine learning explainability and fairness, using true causal counterfactuals.
