mlsanityData to 7 Oct 2026

Correcting Confounding via Random Selection of Background Variables

arXiv 2202.02150 ↗First posted 4 Feb 2022stat.ML cs.LGcited by 2

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Abstract (arXiv)

We propose a method to distinguish causal influence from hidden confounding in the following scenario: given a target variable Y, potential causal drivers X, and a large number of background features, we propose a novel criterion for identifying causal relationship based on the stability of regression coefficients of X on Y with respect to selecting different background features. To this end, we propose a statistic V measuring the coefficient's variability. We prove, subject to a symmetry assumption for the background influence, that V converges to zero if and only if X contains no causal drivers. In experiments with simulated data, the method outperforms state of the art algorithms. Further, we report encouraging results for real-world data. Our approach aligns with the general belief that causal insights admit better generalization of statistical associations across environments, and justifies similar existing heuristic approaches from the literature.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 20 of its 71 references that are in the map. First posted on arXiv 4 Feb 2022.

Builds on

20 of 71 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2022: 2.

2 papers.

  1. 2022Partial Identification of Dose Responses with Hidden Confounderscited by 7
  2. 2022A Consistent Estimator for Confounding Strengthcited by 1