Hierarchical Causal Structure Learning

TL;DR

This paper introduces a novel method for learning causal structures in hierarchical data settings, addressing the gap where existing methods fail due to multi-level organization of variables.

Contribution

It proposes a new approach based on nonlinear structural causal models that handles unobserved confounders and group-specific causal functions in hierarchical data.

Findings

Method effectively learns causal structures in multi-level data.

The approach accommodates unobserved confounders.

The R package HSCM implements the proposed method.

Abstract

Traditional statistical approaches primarily aim to model associations between variables, but many scientific and practical questions require causal methods instead. These approaches rely on assumptions about an underlying structure, often represented by a directed acyclic graph (DAG). When all variables are measured at the same level, causal structures can be learned using existing techniques. However, no suitable methods exist when data are organized hierarchically or across multiple levels. This paper addresses such cases, where both unit-level and group-level variables are present. These multi-level structures frequently arise in fields such as agriculture, where plants (units) grow within different environments (groups). Building on nonlinear structural causal models, or additive noise models, we propose a method that accommodates unobserved confounders as well as group-specific causal functions. The approach is implemented in the R package HSCM, available at https://CRAN.R-project.org/package=HSCM.