This paper concerns the assessment of the effects of actions from a
combination of nonexperimental data and causal assumptions encoded in the form
of a directed acyclic graph in which some variables are presumed to be
unobserved. We provide a procedure that systematically identifies cause effects
between two sets of variables conditioned on some other variables, in time
polynomial in the number of variables in the graph. The identifiable
conditional causal effects are expressed in terms of the observed joint
distribution.

论文探讨了使用非实验数据和因果假设对行动效应进行评估的方法。提供了一种系统识别变量集之间的因果关系的程序，条件是一些变量被视为未观测到。可识别的条件因果效应通过观测到的联合分布来表示。

识别条件因果效应

Identifying Conditional Causal Effects

This paper is concerned with graphical criteria that can be used to solve the
problem of identifying casual effects from nonexperimental data in a causal
Bayesian network structure, i.e., a directed acyclic graph that represents
causal relationships. We first review Pearl's work on this topic [Pearl, 1995],
in which several useful graphical criteria are presented. Then we present a
complete algorithm [Huang and Valtorta, 2006b] for the identifiability problem.
By exploiting the completeness of this algorithm, we prove that the three basic
do-calculus rules that Pearl presents are complete, in the sense that, if a
causal effect is identifiable, there exists a sequence of applications of the
rules of the do-calculus that transforms the causal effect formula into a
formula that only includes observational quantities.

本文研究非实验数据中确定因果关系的图形准则，提出了 Pearl 的工作和 Huang 和 Valtorta 的算法，并证明了 Pearl 提出的基本 do-calculus 规则是完整的。