A General Causal Inference Framework for Cross-Sectional Observational Data
横截面观测数据的通用因果推断框架AI 解读 ↓
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横截面观测数据的通用因果推断框架
- 研究问题
- 面对角色复杂的高维协变量，如何从横截面观测数据中识别真正需要调整的混杂变量，并据此估计因果效应？
- 方法与思路
- 作者基于有向无环图的 Markov 性质，将关键混杂变量联系于共同根祖先，再结合祖先集合识别算法与混杂调整方法。
- 材料陈述的贡献
- 摘要称框架可识别关键混杂变量并获得无偏效应估计，合成数据实验显示精度、稳定性与可解释性的改善。
- 阅读关注点
- 核对共同根祖先结论及因果方向识别的适用假设、未观测变量的处理，以及条件独立检验误差如何传递至效应估计。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已通过同行评审或被期刊接收；摘要未说明保证方向识别和无偏性的完整条件。
原文摘要
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.