Model-based causal feature selection for general response types

TL;DR

This paper introduces a new causal feature selection method called TRAM-ICP that handles various response types, including categorical and count data, using transformation models and invariance testing, with open-source implementation.

Contribution

The paper develops TRAM-ICP, a novel invariant causal prediction method for diverse response types, extending causal discovery to non-continuous and censored data.

Findings

TRAM-ICP effectively identifies causal features across different response types.

The method maintains asymptotic level guarantees in invariance testing.

Open-source R package 'tramicp' facilitates practical application.

Abstract

Discovering causal relationships from observational data is a fundamental yet challenging task. Invariant causal prediction (ICP, Peters et al., 2016) is a method for causal feature selection which requires data from heterogeneous settings and exploits that causal models are invariant. ICP has been extended to general additive noise models and to nonparametric settings using conditional independence tests. However, the latter often suffer from low power (or poor type I error control) and additive noise models are not suitable for applications in which the response is not measured on a continuous scale, but reflects categories or counts. Here, we develop transformation-model (TRAM) based ICP, allowing for continuous, categorical, count-type, and uninformatively censored responses (these model classes, generally, do not allow for identifiability when there is no exogenous heterogeneity). As an invariance test, we propose TRAM-GCM based on the expected conditional covariance between environments and score residuals with uniform asymptotic level guarantees. For the special case of linear shift TRAMs, we also consider TRAM-Wald, which tests invariance based on the Wald statistic. We provide an open-source R package 'tramicp' and evaluate our approach on simulated data and in a case study investigating causal features of survival in critically ill patients.