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.

翻译：跨群体迁移因果信息是临床决策中的关键挑战。因果建模为可识别性和可迁移性提供了判定标准，但这些标准要求已知因果图，而实践中鲜少满足。我们提出一种贝叶斯方法，该方法结合目标域的观测数据与不同域的实验数据来识别s-可容许后门集，从而能够在无需因果图的情况下实现跨群体的无偏因果效应估计。我们证明，若此类集合存在，总能在结果的马尔可夫边界内找到其一，从而缩小搜索空间，并为本方法建立了渐近收敛性保证。我们开发了一种贪婪算法，将可迁移性重构为特征选择问题，通过选择能使给定观测数据下实验数据边际似然最大化的条件集来实现。在模拟和半合成数据中，本方法能正确识别可迁移性偏差，改进因果效应估计，并相较于替代方法表现出优越性能。