Constraint-based causal discovery from limited data is a notoriously
difficult challenge due to the many borderline independence test decisions.
Several approaches to improve the reliability of the predictions by exploiting
redundancy in the independence information have been proposed recently. Though
promising, existing approaches can still be greatly improved in terms of
accuracy and scalability. We present a novel method that reduces the
combinatorial explosion of the search space by using a more coarse-grained
representation of causal information, drastically reducing computation time.
Additionally, we propose a method to score causal predictions based on their
confidence. Crucially, our implementation also allows one to easily combine
observational and interventional data and to incorporate various types of
available background knowledge. We prove soundness and asymptotic consistency
of our method and demonstrate that it can outperform the state-of-the-art on
synthetic data, achieving a speedup of several orders of magnitude. We
illustrate its practical feasibility by applying it on a challenging protein
data set.

提出一种通过利用更粗粒度的原因信息表示来降低搜索空间的组合爆炸，从而极大地减少计算时间，并根据信心对原因预测进行评分的新方法，证明了方法的正确性和渐近一致性，并证明了该方法在合成数据上的优越性能，并将其应用于具有挑战性的蛋白数据集中。

祖先因果推断

Ancestral Causal Inference

In a previous paper [Pearl and Verma, 1991] we presented an algorithm for
extracting causal influences from independence information, where a causal
influence was defined as the existence of a directed arc in all minimal causal
models consistent with the data. In this paper we address the question of
deciding whether there exists a causal model that explains ALL the observed
dependencies and independencies. Formally, given a list M of conditional
independence statements, it is required to decide whether there exists a
directed acyclic graph (dag) D that is perfectly consistent with M, namely,
every statement in M, and no other, is reflected via dseparation in D. We
present and analyze an effective algorithm that tests for the existence of such
a day, and produces one, if it exists.

本文提出了一种用于验证数据一致性的有效算法，该算法可以从独立性信息中提取因果关系，利用条件独立语句测试是否存在符合所有观测到的依赖性和独立性的因果模型，并找出存在的因果关系。