无需因果图，用贝叶斯方法找可迁移的因果效应估计集。
Transportability without Graphs: A Bayesian Approach to Identifying s-Admissible Backdoor Sets
- 将迁移性问题转为特征选择，基于边际似然筛选条件集。
- 在目标结果的马尔可夫边界内总能找到s-可适配后门集。
- 适合临床等缺乏因果图的真实场景，提升跨人群因果推断精度。
跨人群转移因果知识是临床决策中的关键挑战。传统因果建模依赖因果图来判断可识别性和可迁移性，但现实中往往无法获得。本文提出一种贝叶斯方法，结合目标域的观察数据与另一域的实验数据，识别s-可适配后门集，实现跨人群无偏因果效应估计，且无需因果图。我们证明：若存在此类集合，则必存在于结果变量的马尔可夫边界内，从而缩小搜索空间；并建立了该方法的渐近收敛性保证。进一步设计了一种贪心算法，将运输性问题转化为特征选择任务，通过最大化实验数据给定观察数据的边际似然来选择条件集。在模拟与半合成数据上，本方法能准确识别迁移偏差，改善因果效应估计，性能优于现有方法。
原文摘要 · Abstract (English)
Transporting causal information across populations is a critical challenge in clinical decision-making. Causal modeling provides criteria for identifiability and transportability, but these require knowledge of the causal graph, which rarely holds in practice. We propose a Bayesian method that combines observational data from the target domain with experimental data from a different domain to identify s-admissible backdoor sets, which enable unbiased estimation of causal effects across populations, without requiring the causal graph. We prove that if such a set exists, we can always find one within the Markov boundary of the outcome, narrowing the search space, and we establish asymptotic convergence guarantees for our method. We develop a greedy algorithm that reframes transportability as a feature selection problem, selecting conditioning sets that maximize the marginal likelihood of experimental data given observational data. In simulated and semi-synthetic data, our method correctly identifies transportability bias, improves causal effect estimation, and performs favorably against alternatives.
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