We propose Bayesian Hierarchical Invariant Prediction (BHIP) reframing Invariant Causal Prediction (ICP) through the lens of Hierarchical Bayes. We leverage the hierarchical structure to explicitly test invariance of causal mechanisms under heterogeneous data, resulting in improved computational scalability for a larger number of predictors compared to ICP. Moreover, given its Bayesian nature BHIP enables the use of prior information. We evaluate BHIP on both synthetic and real-world datasets, demonstrating its potential as an alternative inference method to ICP and related methods.

翻译：我们提出贝叶斯分层不变性预测（BHIP），通过分层贝叶斯视角重新构建不变因果预测（ICP）框架。利用分层结构显式检验异质性数据中因果机制的不变性，相比ICP，该方法在处理更多预测变量时具有更好的计算可扩展性。此外，基于其贝叶斯特性，BHIP能够利用先验信息。我们在合成数据集和真实数据集上评估了BHIP，展示了其作为ICP及相关方法替代推断方案的潜力。