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Efficient Identification of Direct Causal Parents via Invariance and Minimum Error Testing

arXiv 2409.12797 ↗First posted 19 Sep 2024cs.LG cs.AIcited by 0

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Abstract (arXiv)

Invariant causal prediction (ICP) is a popular technique for finding causal parents (direct causes) of a target via exploiting distribution shifts and invariance testing (Peters et al., 2016). However, since ICP needs to run an exponential number of tests and fails to identify parents when distribution shifts only affect a few variables, applying ICP to practical large scale problems is challenging. We propose MMSE-ICP and fastICP, two approaches which employ an error inequality to address the identifiability problem of ICP. The inequality states that the minimum prediction error of the predictor using causal parents is the smallest among all predictors which do not use descendants. fastICP is an efficient approximation tailored for large problems as it exploits the inequality and a heuristic to run fewer tests. MMSE-ICP and fastICP not only outperform competitive baselines in many simulations but also achieve state-of-the-art result on a large scale real data benchmark.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 27 of its 67 references that are in the map. First posted on arXiv 19 Sep 2024.

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27 of 67 references.

References · 27

  1. 2019Invariant Risk Minimizationcited by 1,832
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  12. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
15 more
  1. 2017Permutation-based Causal Inference Algorithms with Interventionscited by 78
  2. 2019Permutation-Based Causal Structure Learning with Unknown Intervention Targetscited by 69
  3. 2020A causal framework for distribution generalizationcited by 68
  4. 2018Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolutioncited by 66
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  7. 2020Active Invariant Causal Prediction: Experiment Selection through Stabilitycited by 37
  8. 2013Jointly interventional and observational data: estimation of interventional Markov equivalence classes of directed acyclic graphscited by 25
  9. 2020Causal Feature Selection via Orthogonal Searchcited by 11
  10. 2021Towards Efficient Local Causal Structure Learningcited by 7
  11. 2021Variance Minimization in the Wasserstein Space for Invariant Causal Predictioncited by 5
  12. 2022Invariant Ancestry Searchcited by 5
  13. 2024Knockout: A simple way to handle missing inputscited by 4
  14. 2023Robust Learning via Conditional Prevalence Adjustmentcited by 2
  15. 2024Adapting to Shifting Correlations with Unlabeled Data Calibrationcited by 2

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