Corpus ID: 220381228

Causal Feature Selection via Orthogonal Search

  title={Causal Feature Selection via Orthogonal Search},
  author={Anant Raj and S. Bauer and Ashkan Soleymani and M. Besserve and B. Sch{\"o}lkopf},
  • Anant Raj, S. Bauer, +2 authors B. Schölkopf
  • Published 2020
  • Mathematics, Computer Science
  • ArXiv
  • The problem of inferring the direct causal parents of a response variable among a large set of explanatory variables is of high practical importance in many disciplines. Recent work in the field of causal discovery exploits invariance properties of models across different experimental conditions for detecting direct causal links. However, these approaches generally do not scale well with the number of explanatory variables, are difficult to extend to nonlinear relationships, and require data… CONTINUE READING

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    Learning Causal Structures Using Regression Invariance
    • 19
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    Causal Feature Selection
    • 91
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    Causal discovery with continuous additive noise models
    • 214
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    Causal inference using invariant prediction: identification and confidence intervals
    • 208
    • PDF
    A Linear Non-Gaussian Acyclic Model for Causal Discovery
    • 717
    • Highly Influential
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    Double machine learning for treatment and causal parameters
    • 127
    • Highly Influential
    • PDF
    A Unified View of Causal and Non-causal Feature Selection
    • 8
    • PDF
    Selecting causal brain features with a single conditional independence test per feature
    • 6
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    Nonlinear causal discovery with additive noise models
    • 494
    • PDF