mlsanityData to 14 Oct 2026

Bayesian Hierarchical Invariant Prediction

arXiv 2505.11211 ↗First posted 16 May 2025cs.LG cs.AI stat.MEcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 17 of its 50 references that are in the map. First posted on arXiv 16 May 2025.

Builds on

17 of 50 references.

References · 17

  1. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 536
  2. 2018Pyro: Deep Universal Probabilistic Programmingcited by 380
  3. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  4. 2019Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyrocited by 191
  5. 2017Invariant Causal Prediction for Nonlinear Modelscited by 140
  6. 2018Invariance, Causality and Robustnesscited by 123
  7. 2017Invariant Causal Prediction for Sequential Datacited by 63
  8. 2022Causal de Finetti: On the Identification of Invariant Causal Structure in Exchangeable Datacited by 42
  9. 2020Active Invariant Causal Prediction: Experiment Selection through Stabilitycited by 37
  10. 2022BaCaDI: Bayesian Causal Discovery with Unknown Interventionscited by 26
  11. 2021Invariant Policy Learning: A Causal Perspectivecited by 17
  12. 2023Environment Invariant Linear Least Squarescited by 17
5 more
  1. 2018Learning stable and predictive structures in kinetic systems: Benefits of a causal approachcited by 12
  2. 2022Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventionscited by 12
  3. 2024Do Finetti: On Causal Effects for Exchangeable Datacited by 11
  4. 2024Hierarchical Causal Modelscited by 9
  5. 2023Model-based causal feature selection for general response typescited by 3

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Bayesian Invariance Modeling of Multi-Environment Datacited by 2