Predicting the effect of unseen interventions is a fundamental research
question across the data sciences. It is well established that, in general,
such questions cannot be answered definitively from observational data, e.g.,
as a consequence of unobserved confounding. A generalization of this task is to
determine non-trivial bounds on causal effects induced by the data, also known
as the task of partial causal identification. In the literature, several
algorithms have been developed for solving this problem. Most, however, require
a known parametric form or a fully specified causal diagram as input, which is
usually not available in practical applications. In this paper, we assume as
input a less informative structure known as a Partial Ancestral Graph, which
represents a Markov equivalence class of causal diagrams and is learnable from
observational data. In this more "data-driven" setting, we provide a systematic
algorithm to derive bounds on causal effects that can be computed analytically.

预测未见干预的效果是数据科学中的一个基本研究问题。本文采用了部分祖先图（Partial Ancestral Graph）作为输入，提供了一种可以在数据驱动环境下计算因果效应界限的系统算法。