Structural causal models (SCMs) provide a principled approach to identifying causation from observational and experimental data in disciplines ranging from economics to medicine. SCMs, however, require domain knowledge, which is typically represented as graphical models. A key challenge in this context is the absence of a methodological framework for encoding priors (background knowledge) into causal models in a systematic manner. We propose an abstraction called causal knowledge hierarchy (CKH) for encoding priors into causal models. Our approach is based on the foundation of "levels of evidence" in medicine, with a focus on confidence in causal information. Using CKH, we present a methodological framework for encoding causal priors from various data sources and combining them to derive an SCM. We evaluate our approach on a simulated dataset and demonstrate overall performance compared to the ground truth causal model with sensitivity analysis.

在各个学科中，结构因果模型(SCMs)为从观察和实验数据中识别因果关系提供了一个有原则的方法。然而，SCMs通常被表示为图形模型，不能仅依赖于数据，而需要领域知识的支持。本文提出了一个称为因果知识层次结构 (CKH) 的抽象概念，用于将先验知识编码成因果模型。我们的方法基于医学中“证据水平”的基础，并侧重于对因果信息的置信度。使用CKH，我们提出了一个将各种信息源的因果先验知识编码并结合起来推导SCM的方法论框架。我们在模拟数据集上评估了我们的方法，并通过灵敏度分析展示了其整体性能相对于基础真实因果模型的性能。

CKH: 从数据和先验信息中估计结构因果模型的因果知识层次结构