用贝叶斯层次模型改进因果预测，提升可扩展性与先验利用能力。
Bayesian Hierarchical Invariant Prediction
- 基于层次贝叶斯框架重构建模因果不变性
- 在异质数据下实现更多变量的高效因果推断
- 支持先验信息融合，适合有领域知识场景
我们提出贝叶斯层次不变预测（BHIP），从层次贝叶斯视角重构不变因果预测（ICP）。通过引入层次结构，显式检验异质数据下的因果机制不变性，相较于ICP在更大数量预测变量下表现出更好的计算可扩展性。此外，其贝叶斯特性允许引入先验信息。我们在合成数据和真实数据集上评估了BHIP，验证其作为ICP及相关方法的替代推断工具的潜力。
原文摘要 · Abstract (English)
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.
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