标题： 干预贝叶斯高斯等价得分用于具有未知软干预的贝叶斯因果推断 显示英文标题

标题： The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions

摘要： 描述控制系统的因果关系是许多科学领域中的基本任务，理想情况下应通过实验研究来解决。 然而，在干预情景下获取数据可能并不总是可行的，而仅从观察数据中发现因果关系则非常具有挑战性。 在某些情况下，例如基因组学，我们可能有来自异质研究条件的数据，其中只有部分干预涉及研究变量的子集，其影响和目标可能未知。 结合实验和观察研究的数据提供了利用两个领域并改进因果结构可识别性的机会。 为此，我们定义了混合观察和干预数据的干预BGe评分，其中干预的目标和影响可能未知。 为了展示该方法，我们将它的性能与其他最先进的算法进行比较，包括在模拟和数据分析应用中。 我们方法的优势在于它采用贝叶斯视角，从而对DAG结构的后验分布进行全面表征。 给定一个DAG样本，还可以自动生成干预效果的完整后验分布。 因此，该方法有效地捕捉了结构和参数估计中的不确定性。 用于重现模拟和分析的代码可在github.com/jackkuipers/iBGe公开获得。 显示英文摘要

摘要： Describing the causal relations governing a system is a fundamental task in many scientific fields, ideally addressed by experimental studies. However, obtaining data under intervention scenarios may not always be feasible, while discovering causal relations from purely observational data is notoriously challenging. In certain settings, such as genomics, we may have data from heterogeneous study conditions, with soft (partial) interventions only pertaining to a subset of the study variables, whose effects and targets are possibly unknown. Combining data from experimental and observational studies offers the opportunity to leverage both domains and improve on the identifiability of causal structures. To this end, we define the interventional BGe score for a mixture of observational and interventional data, where the targets and effects of intervention may be unknown. To demonstrate the approach we compare its performance to other state-of-the-art algorithms, both in simulations and data analysis applications. Prerogative of our method is that it takes a Bayesian perspective leading to a full characterisation of the posterior distribution of the DAG structures. Given a sample of DAGs one can also automatically derive full posterior distributions of the intervention effects. Consequently the method effectively captures the uncertainty both in the structure and the parameter estimates. Codes to reproduce the simulations and analyses are publicly available at github.com/jackkuipers/iBGe