Efficient Identification of Direct Causal Parents via Invariance and Minimum Error Testing

TL;DR

This paper introduces MMSE-ICP and fastICP, innovative methods that improve the efficiency and scalability of invariant causal prediction for identifying direct causal parents, especially in large-scale problems.

Contribution

The paper proposes MMSE-ICP and fastICP, new approaches that use an error inequality to enhance ICP's identifiability and computational efficiency in large-scale causal discovery.

Findings

MMSE-ICP and fastICP outperform existing methods in simulations.

They achieve state-of-the-art results on large-scale real data.

fastICP reduces the number of tests needed for causal identification.

Abstract

Invariant causal prediction (ICP) is a popular technique for finding causal parents (direct causes) of a target via exploiting distribution shifts and invariance testing (Peters et al., 2016). However, since ICP needs to run an exponential number of tests and fails to identify parents when distribution shifts only affect a few variables, applying ICP to practical large scale problems is challenging. We propose MMSE-ICP and fastICP, two approaches which employ an error inequality to address the identifiability problem of ICP. The inequality states that the minimum prediction error of the predictor using causal parents is the smallest among all predictors which do not use descendants. fastICP is an efficient approximation tailored for large problems as it exploits the inequality and a heuristic to run fewer tests. MMSE-ICP and fastICP not only outperform competitive baselines in many simulations but also achieve state-of-the-art result on a large scale real data benchmark.