Speaker |
Ricardo Pio Monti |
|---|---|
Affiliation |
UCL (Gatsby) |
Date |
Friday, 14 December 2018 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
Roberts G08 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
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. |
Biography |