Joint estimation of linear non-Gaussian acyclic models

TL;DR

This paper introduces a method for jointly estimating multiple LiNGAM causal models across different datasets, assuming shared causal orderings but differing connection strengths and distributions, improving accuracy over separate estimations.

Contribution

The paper presents a novel approach to jointly estimate multiple LiNGAMs with shared causal orderings, accommodating different connection strengths and distributions across datasets.

Findings

Joint estimation improves accuracy over separate estimation.

Method effectively handles multiple datasets with shared causal structure.

Simulations demonstrate superior performance of the proposed method.

Abstract

A linear non-Gaussian structural equation model called LiNGAM is an identifiable model for exploratory causal analysis. Previous methods estimate a causal ordering of variables and their connection strengths based on a single dataset. However, in many application domains, data are obtained under different conditions, that is, multiple datasets are obtained rather than a single dataset. In this paper, we present a new method to jointly estimate multiple LiNGAMs under the assumption that the models share a causal ordering but may have different connection strengths and differently distributed variables. In simulations, the new method estimates the models more accurately than estimating them separately.