Additive noise models

Algorithm Introduction

Causal discovery based on the additive noise models (ANM [1]). If you would like to apply the method to more than two variables, we suggest you first apply the PC algorithm and then use pair-wise analysis in this implementation to find the causal directions that cannot be determined by PC.

Usage

from causallearn.search.FCMBased.ANM.ANM import ANM
anm = ANM()
p_value_foward, p_value_backward = anm.cause_or_effect(data_x, data_y)

Parameters

data_x: input data (n, 1).

data_y: output data (n, 1).

Returns

pval_forward: p value in the x->y direction.

pval_backward: p value in the y->x direction.

[1]

Hoyer, P. O., Janzing, D., Mooij, J. M., Peters, J., & Schölkopf, B. (2008, December). Nonlinear causal discovery with additive noise models. In NIPS (Vol. 21, pp. 689-696).