Log In Sign Up

The Effect of Noise Level on Causal Identification with Additive Noise Models

by   Benjamin Kap, et al.

In recent years a lot of research has been conducted within the area of causal inference and causal learning. Many methods have been developed to identify the cause-effect pairs in models and have been successfully applied to observational real-world data in order to determine the direction of causal relationships. Many of these methods require simplifying assumptions, such as absence of confounding, cycles, and selection bias. Yet in bivariate situations causal discovery problems remain challenging. One class of such methods, that also allows tackling the bivariate case, is based on Additive Noise Models (ANMs). Unfortunately, one aspect of these methods has not received much attention until now: what is the impact of different noise levels on the ability of these methods to identify the direction of the causal relationship. This work aims to bridge this gap with the help of an empirical study. For this work, we considered bivariate cases, which is the most elementary form of a causal discovery problem where one needs to decide whether X causes Y or Y causes X, given joint distributions of two variables X, Y. Furthermore, two specific methods have been selected, Regression with Subsequent Independence Test and Identification using Conditional Variances, which have been tested with an exhaustive range of ANMs where the additive noises' levels gradually change from 1 (the latter remains fixed). Additionally, the experiments in this work consider several different types of distributions as well as linear and non-linear ANMs. The results of the experiments show that these methods can fail to capture the true causal direction for some levels of noise.


page 1

page 2

page 3

page 4


Causal Identification with Additive Noise Models: Quantifying the Effect of Noise

In recent years, a lot of research has been conducted within the area of...

Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is ...

Causal Discovery with General Non-Linear Relationships Using Non-Linear ICA

We consider the problem of inferring causal relationships between two or...

Beware of the Simulated DAG! Varsortability in Additive Noise Models

Additive noise models are a class of causal models in which each variabl...

Parallel ensemble methods for causal direction inference

Inferring the causal direction between two variables from their observat...

Score-based Causal Learning in Additive Noise Models

Given data sampled from a number of variables, one is often interested i...

Removing systematic errors for exoplanet search via latent causes

We describe a method for removing the effect of confounders in order to ...