Leveraging directed causal discovery to detect latent common causes

10/22/2019
by   Ciarán M. Lee, et al.
0

The discovery of causal relationships is a fundamental problem in science and medicine. In recent years, many elegant approaches to discovering causal relationships between two variables from uncontrolled data have been proposed. However, most of these deal only with purely directed causal relationships and cannot detect latent common causes. Here, we devise a general method which takes a purely directed causal discovery algorithm and modifies it so that it can also detect latent common causes. The identifiability of the modified algorithm depends on the identifiability of the original, as well as an assumption that the strength of noise be relatively small. We apply our method to two directed causal discovery algorithms, the Information Geometric Causal Inference of (Daniusis et al., 2010) and the Kernel Conditional Deviance for Causal Inference of (Mitrovic, Sejdinovic, and Teh, 2018), and extensively test on synthetic data—detecting latent common causes in additive, multiplicative and complex noise regimes—and on real data, where we are able to detect known common causes. In addition to detecting latent common causes, our experiments demonstrate that both modified algorithms preserve the performance of the original directed algorithm in distinguishing directed causal relations.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/11/2014

Distinguishing cause from effect using observational data: methods and benchmarks

The discovery of causal relationships from purely observational data is ...
research
05/12/2015

Removing systematic errors for exoplanet search via latent causes

We describe a method for removing the effect of confounders in order to ...
research
08/20/2018

Discovering Context Specific Causal Relationships

With the increasing need of personalised decision making, such as person...
research
04/22/2018

Causal network discovery by iterative conditioning: comparison of algorithms

Estimating causal interactions in complex networks is an important probl...
research
08/14/2019

Causal discovery in heavy-tailed models

Causal questions are omnipresent in many scientific problems. While much...
research
05/05/2023

Causal Discovery with Stage Variables for Health Time Series

Using observational data to learn causal relationships is essential when...
research
09/04/2019

Likelihood-Free Overcomplete ICA and Applications in Causal Discovery

Causal discovery witnessed significant progress over the past decades. I...

Please sign up or login with your details

Forgot password? Click here to reset