Seminar: Causal discovery with general non-linear relationships using non-linear ICA

SpeakerRicardo Pio Monti
AffiliationUCL (Gatsby)
DateFriday, 14 Dec 2018
Time13:00 - 14:00
LocationRoberts G08
Event seriesDeepMind CSML Seminar Series

We consider the bivariate causal discovery problem - this corresponds to inferring the causal relationship between two passively observed variables. While this problem has been extensively studied, the majority of current methods assume a linear causal relationship, and the few methods which consider non-linear dependencies usually make the assumption of additive noise. Here, we propose a framework through which we can perform causal discovery in the presence of general non-linear relationships. The proposed method exploits a correspondence between a piecewise stationary non-linear ICA model and non-linear causal models. We show that in the case of bivariate causal discovery, non-linear ICA can be used to infer the causal direction via a series of independence tests. A series of experiments on simulated data demonstrate the capabilities of the proposed method. Extensions to multivariate causal discovery are also discussed.

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