Integer Programming for Causal Structure Learning in the Presence of Latent Variables

TL;DR

This paper introduces an exact integer programming approach for learning ancestral ADMGs in causal inference with latent variables, outperforming existing heuristic methods in accuracy and efficiency for medium-sized problems.

Contribution

It develops a novel IP formulation with valid inequalities for optimal ADMG learning, extending DAG IP models to handle latent variables.

Findings

Efficiently solves medium-sized problems.

Achieves higher accuracy than existing methods.

Outperforms benchmark constraint-based approaches.

Abstract

The problem of finding an ancestral acyclic directed mixed graph (ADMG) that represents the causal relationships between a set of variables is an important area of research on causal inference. Most existing score-based structure learning methods focus on learning directed acyclic graph (DAG) models without latent variables. A number of score-based methods have recently been proposed for the ADMG learning, yet they are heuristic in nature and do not guarantee an optimal solution. We propose a novel exact score-based method that solves an integer programming (IP) formulation and returns a score-maximizing ancestral ADMG for a set of continuous variables that follow a multivariate Gaussian distribution. We generalize the state-of-the-art IP model for DAG learning problems and derive new classes of valid inequalities to formulate an IP model for ADMG learning. Empirically, our model can be solved efficiently for medium-sized problems and achieves better accuracy than state-of-the-art score-based methods as well as benchmark constraint-based methods.