Causal Feature Selection via Orthogonal Search

Understand

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 {\em 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.