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Invariant Causal Prediction with Local Models

arXiv 2401.05218 ↗First posted 10 Jan 2024cs.LG stat.MEcited by 0

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Abstract (arXiv)

We consider the task of identifying the causal parents of a target variable among a set of candidates from observational data. Our main assumption is that the candidate variables are observed in different environments which may, under certain assumptions, be regarded as interventions on the observed system. We assume a linear relationship between target and candidates, which can be different in each environment with the only restriction that the causal structure is invariant across environments. Within our proposed setting we provide sufficient conditions for identifiability of the causal parents and introduce a practical method called L-ICP (Localized Invariant Causal Prediction), which is based on a hypothesis test for parent identification using a ratio of minimum and maximum statistics. We then show in a simplified setting that the statistical power of L-ICP converges exponentially fast in the sample size, and finally we analyze the behavior of L-ICP experimentally in more general settings.

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References · 12

  1. 2019Invariant Risk Minimizationcited by 1,832
  2. 2015Causal inference using invariant prediction: identification and confidence intervalscited by 533
  3. 2017Detecting causal associations in large nonlinear time series datasetscited by 240
  4. 2019Causal Discovery from Heterogeneous/Nonstationary Data with Independent Changescited by 166
  5. 2017Invariant Causal Prediction for Nonlinear Modelscited by 140
  6. 2017Invariant Causal Prediction for Sequential Datacited by 63
  7. 2019Causal Discovery and Forecasting in Nonstationary Environments with State-Space Modelscited by 45
  8. 2022Causal Discovery with Heterogeneous Observational Datacited by 13
  9. 2021Multi-task Learning of Order-Consistent Causal Graphscited by 10
  10. 2018Switching Regression Models and Causal Inference in the Presence of Discrete Latent Variablescited by 4
  11. 2022Identifying the Causes of Pyrocumulonimbus (PyroCb)cited by 4
  12. 2020Anomaly Detection for a Large Number of Streams: A Permutation-Based Higher Criticism Approachcited by 1

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