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Causal Effect Identification in LvLiNGAM From Higher-Order Cumulants

MCML Authors

Daniele Tramontano

→ Group Mathias Drton
Mathematical Statistics

Mathias Drton

Prof. Dr.

Principal Investigator

Mathematical Statistics

Abstract

This paper investigates causal effect identification in latent variable Linear Non-Gaussian Acyclic Models (lvLiNGAM) using higher-order cumulants, addressing two prominent setups that are challenging in the presence of latent confounding: (1) a single proxy variable that may causally influence the treatment and (2) underspecified instrumental variable cases where fewer instruments exist than treatments. We prove that causal effects are identifiable with a single proxy or instrument and provide corresponding estimation methods. Experimental results demonstrate the accuracy and robustness of our approaches compared to existing methods, advancing the theoretical and practical understanding of causal inference in linear systems with latent confounders.

inproceedings