Invariant Ancestry Search

TL;DR

This paper introduces invariant ancestry search (IAS), a method leveraging invariance principles to identify causal ancestors with guarantees, scalable algorithms, and demonstrated effectiveness on simulated and real datasets.

Contribution

The paper proposes IAS, a novel approach that extends invariant causal prediction by focusing on minimal invariance to better identify causal ancestors.

Findings

IAS contains only true ancestors asymptotically.

Algorithms are scalable and effective on real data.

IAS outperforms existing methods in causal discovery.

Abstract

Recently, methods have been proposed that exploit the invariance of prediction models with respect to changing environments to infer subsets of the causal parents of a response variable. If the environments influence only few of the underlying mechanisms, the subset identified by invariant causal prediction (ICP), for example, may be small, or even empty. We introduce the concept of minimal invariance and propose invariant ancestry search (IAS). In its population version, IAS outputs a set which contains only ancestors of the response and is a superset of the output of ICP. When applied to data, corresponding guarantees hold asymptotically if the underlying test for invariance has asymptotic level and power. We develop scalable algorithms and perform experiments on simulated and real data.