Causal inference methods for observational data are highly regarded due to
their wide applicability. While there are already numerous methods available
for de-confounding bias, these methods generally assume that covariates consist
solely of confounders or make naive assumptions about the covariates. Such
assumptions face challenges in both theory and practice, particularly when
dealing with high-dimensional covariates. Relaxing these naive assumptions and
identifying the confounding covariates that truly require correction can
effectively enhance the practical significance of these methods. Therefore,
this paper proposes a General Causal Inference (GCI) framework specifically
designed for cross-sectional observational data, which precisely identifies the
key confounding covariates and provides corresponding identification algorithm.
Specifically, based on progressive derivations of the Markov property on
Directed Acyclic Graph, we conclude that the key confounding covariates are
equivalent to the common root ancestors of the treatment and the outcome
variable. Building upon this conclusion, the GCI framework is composed of a
novel Ancestor Set Identification (ASI) algorithm and de-confounding inference
methods. Firstly, the ASI algorithm is theoretically supported by the
conditional independence properties and causal asymmetry between variables,
enabling the identification of key confounding covariates. Subsequently, the
identified confounding covariates are used in the de-confounding inference
methods to obtain unbiased causal effect estimation, which can support informed
decision-making. Extensive experiments on synthetic datasets demonstrate that
the GCI framework can effectively identify the critical confounding covariates
and significantly improve the precision, stability, and interpretability of
causal inference in observational studies.

基于进化驱动的有向无回路图的马尔可夫属性，本文提出了一种针对横断面观测数据的通用因果推断框架 (GCI)，能准确识别关键混杂变量并提供相应的识别算法，从而有效改善观察研究中因果推断的精确性、稳定性和可解释性。

一个通用因果推断框架用于横断面观察数据

A General Causal Inference Framework for Cross-Sectional Observational  Data

Inferring causal relationships from observational data is rarely
straightforward, but the problem is especially difficult in high dimensions.
For these applications, causal discovery algorithms typically require
parametric restrictions or extreme sparsity constraints. We relax these
assumptions and focus on an important but more specialized problem, namely
recovering the causal order among a subgraph of variables known to descend from
some (possibly large) set of confounding covariates, i.e. a $\textit{confounder
blanket}$. This is useful in many settings, for example when studying a dynamic
biomolecular subsystem with genetic data providing background information.
Under a structural assumption called the $\textit{confounder blanket
principle}$, which we argue is essential for tractable causal discovery in high
dimensions, our method accommodates graphs of low or high sparsity while
maintaining polynomial time complexity. We present a structure learning
algorithm that is provably sound and complete with respect to a so-called
$\textit{lazy oracle}$. We design inference procedures with finite sample error
control for linear and nonlinear systems, and demonstrate our approach on a
range of simulated and real-world datasets. An accompanying $\texttt{R}$
package, $\texttt{cbl}$, is available from $\texttt{CRAN}$.

本研究提出一种名为 “混淆毯原则” 的结构假设，使得我们设计的因果关系推断算法能够适用于高维数据，同时保持多项式复杂度。我们演示了我们的方法在模拟和真实数据集上的运作，包括一个用于线性和非线性系统的推断过程和有限样本误差控制。