Causal Effect Identification in lvLiNGAM from Higher-Order Cumulants
It addresses causal inference problems in linear systems with latent confounders for researchers, offering theoretical advances but appearing incremental in method.
This paper tackles causal effect identification in latent variable linear non-Gaussian models with challenging latent confounding scenarios, proving identifiability with a single proxy or instrument and showing experimental accuracy and robustness improvements over existing methods.
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.