Bayesian Hierarchical Invariant Prediction
This work addresses computational bottlenecks in causal inference for researchers, but it is incremental as it builds directly on existing ICP methods.
The authors tackled the problem of scaling Invariant Causal Prediction (ICP) by proposing Bayesian Hierarchical Invariant Prediction (BHIP), which reframes ICP using Hierarchical Bayes to test invariance under heterogeneous data, resulting in improved computational scalability for a larger number of predictors.
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.