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Latent Causation: An algorithm for pairs of correlated latent variables in Linear Non-Gaussian Structural Equation Modeling

par Pollaris, Arnaud ;Bontempi, Gianluca
Référence BNAIC/BENELEARN (19 & 20 November 2020: Leiden (online))
Publication Non publié, 2020-10-26

Communication à un colloque

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Résumé :

This paper addresses the problem of inferring causation in a pair of linearly correlated continuous latent variables. We first discuss the limitations of the Direction Dependance Analysis (DDA) approach and then introduce the Latent Causation (LC). Five variants (in terms of dependency statistic) of the LC algorithm are assessed with ROC curves, then we consider the case of a latent confounder (uniform or chi-square distributed). While the distribution and the correlations of the latent confounder influence the accuracy, experimental results show the robustness of the method using bootstrapped p-values. Implications and limits of the experimental results are then discussed together with future directions.