Causal Feature Selection via Orthogonal Search
Abstract: 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. However, established approaches often scale at least exponentially with the number of explanatory variables, are difficult to extend to nonlinear relationships and are difficult to extend to cyclic data. Inspired by debiased machine learning methods, we study a one-vs.-the-rest feature selection approach to discover the direct causal parent of the response. We propose an algorithm that works for purely observational data while also offering theoretical guarantees, including the case of partially nonlinear relationships possibly under the presence of cycles. As it requires only one estimation for each variable, our approach is applicable even to large graphs. We demonstrate significant improvements compared to established approaches.
Submission Length: Regular submission (no more than 12 pages of main content)
Previous TMLR Submission Url: https://openreview.net/forum?id=NQUy27xqYX&referrer=%5BAuthor%20Console%5D(%2Fgroup%3Fid%3DTMLR%2FAuthors%23your-submissions)
Changes Since Last Submission: A brief discussion on comparing statistical settings of our approach with debiased LASSO is added to related work. Faithfulness and method assumption concerns were addressed in the previous revision.
Code: https://github.com/Ashkan-Soleymani98/CORTH-Features-Causal-Feature-Selection-via-Orthogonal-Search
Assigned Action Editor: ~Yingzhen_Li1
License: Creative Commons Attribution 4.0 International (CC BY 4.0)
Submission Number: 104
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