论文标题
BCDAG：用于贝叶斯结构的R包装和高斯dag的因果学习
BCDAG: An R package for Bayesian structure and Causal learning of Gaussian DAGs
论文作者
论文摘要
定向的无环图（DAG）为建模多元设置中变量之间的因果关系提供了一个强大的框架；另外，通过DO-Calculus理论，它们允许从纯观测数据中识别和估计变量之间的因果关系。在这种情况下，从数据中推断DAG结构的过程称为因果结构学习或因果发现。我们介绍了BCDAG，这是一个用于Gaussian观察数据的贝叶斯因果发现和因果效应估计的R包，并实施了由Castelletti＆Mascaro（2021）提出的Markov Chain Monte Carlo（MCMC）计划。我们的实现有效地缩放了观测值的数量，并且每当DAG足够稀疏时，数据集中的变量数量。该软件包还提供了用于收敛诊断的功能，以及可视化和总结后推理的功能。在本文中，我们介绍了基础方法的关键特征以及其在BCDAG中的实现。然后，我们在真实和模拟数据集上说明了主要功能和算法。
Directed Acyclic Graphs (DAGs) provide a powerful framework to model causal relationships among variables in multivariate settings; in addition, through the do-calculus theory, they allow for the identification and estimation of causal effects between variables also from pure observational data. In this setting, the process of inferring the DAG structure from the data is referred to as causal structure learning or causal discovery. We introduce BCDAG, an R package for Bayesian causal discovery and causal effect estimation from Gaussian observational data, implementing the Markov chain Monte Carlo (MCMC) scheme proposed by Castelletti & Mascaro (2021). Our implementation scales efficiently with the number of observations and, whenever the DAGs are sufficiently sparse, with the number of variables in the dataset. The package also provides functions for convergence diagnostics and for visualizing and summarizing posterior inference. In this paper, we present the key features of the underlying methodology along with its implementation in BCDAG. We then illustrate the main functions and algorithms on both real and simulated datasets.
