Related papers: Causal Effect Identification in LiNGAM Models with Latent Confounders

Causal Effect Identification in LiNGAM Models with Latent Confounders

URL: http://arxiv.org/abs/2406.02049v1
Date: Tue, 4 Jun 2024 07:30:27 GMT
Title: Causal Effect Identification in LiNGAM Models with Latent Confounders
Authors: Daniele Tramontano, Yaroslav Kivva, Saber Salehkaleybar, Mathias Drton, Negar Kiyavash,
Abstract summary: We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables.
Score: 20.751445296400316
License: http://creativecommons.org/licenses/by/4.0/
Abstract: We study the generic identifiability of causal effects in linear non-Gaussian acyclic models (LiNGAM) with latent variables. We consider the problem in two main settings: When the causal graph is known a priori, and when it is unknown. In both settings, we provide a complete graphical characterization of the identifiable direct or total causal effects among observed variables. Moreover, we propose efficient algorithms to certify the graphical conditions. Finally, we propose an adaptation of the reconstruction independent component analysis (RICA) algorithm that estimates the causal effects from the observational data given the causal graph. Experimental results show the effectiveness of the proposed method in estimating the causal effects.

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