论文标题
通过保存干预措施来量化内在因果贡献
Quantifying intrinsic causal contributions via structure preserving interventions
论文作者
论文摘要
我们提出了一个因果影响的概念，该概念描述了节点对DAG中目标节点的贡献的“内在”部分。通过递归将每个节点作为上游噪声项的函数，我们将每个节点添加的固有信息与从其祖先获得的信息分开。为了将固有信息解释为{\ it因果关系}的贡献，我们考虑以“结构性的干预措施”，以模仿对父母的通常依赖性并且不会扰动观察到的关节分布的方式将每个节点随机化。为了获得相对于重新标记节点不变的度量，我们使用基于沙普利的对称性化，并表明将目标节点分解为噪声变量后，在线性情况下将其减少到简单的ANOVA。我们描述了我们对方差和熵的贡献分析，但是可以类似地定义其他目标指标的贡献。该代码可在开源库Dowhy的软件包中可用。
We propose a notion of causal influence that describes the `intrinsic' part of the contribution of a node on a target node in a DAG. By recursively writing each node as a function of the upstream noise terms, we separate the intrinsic information added by each node from the one obtained from its ancestors. To interpret the intrinsic information as a {\it causal} contribution, we consider `structure-preserving interventions' that randomize each node in a way that mimics the usual dependence on the parents and does not perturb the observed joint distribution. To get a measure that is invariant with respect to relabelling nodes we use Shapley based symmetrization and show that it reduces in the linear case to simple ANOVA after resolving the target node into noise variables. We describe our contribution analysis for variance and entropy, but contributions for other target metrics can be defined analogously. The code is available in the package gcm of the open source library DoWhy.
