The standard approach to answering an identifiable causal-effect query (e.g., $P(Y|do(X)$) when given a causal diagram and observational data is to first generate an estimand, or probabilistic expression over the observable variables, which is then evaluated using the observational data. In this paper, we propose an alternative paradigm for answering causal-effect queries over discrete observable variables. We propose to instead learn the causal Bayesian network and its confounding latent variables directly from the observational data. Then, efficient probabilistic graphical model (PGM) algorithms can be applied to the learned model to answer queries. Perhaps surprisingly, we show that this \emph{model completion} learning approach can be more effective than estimand approaches, particularly for larger models in which the estimand expressions become computationally difficult. We illustrate our method's potential using a benchmark collection of Bayesian networks and synthetically generated causal models.

本研究针对从观察数据中回答因果效应查询的标准方法的不足，提出了一种新的范式，直接从观察数据中学习因果贝叶斯网络及其潜在混杂变量。研究发现，这种模型补全学习方法在较大模型中比传统的估计方法更为有效，具有重要的应用潜力。

从学习到的因果网络估计因果效应