Skip to content
conftrace_
2015 ICML ICML 2015

Removing systematic errors for exoplanet search via latent causes

Abstract

We describe a method for removing the effect of confounders in order to reconstruct a latent quantity of interest. The method, referred to as half-sibling regression, is inspired by recent work in causal inference using additive noise models. We provide a theoretical justification and illustrate the potential of the method in a challenging astronomy application.

🌉 Interdisciplinary Bridge - Artificial Intelligence and Knowledge & Reasoning
🐣 Hot Topic Early Bird - causal inference

Authors

Bernhard Schölkopf, David Hogg, Dun Wang, Dan Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters

Topics

Artificial Intelligence > Core AI > Causal Inference Knowledge & Reasoning > Reasoning > Causal Inference

Keywords

causal inference latent variable additive noise model confounder removal exoplanet search
Download PDF

More from ICML 2015

All ICML 2015 papers →
Online Time Series Prediction with Missing Data
Proteins, Particles, and Pseudo-Max-Marginals: A Submodular Approach
A Fast Variational Approach for Learning Markov Random Field Language Models
Scalable Nonparametric Bayesian Inference on Point Processes with Gaussian Processes
Correlation Clustering in Data Streams
Learning Scale-Free Networks by Dynamic Node Specific Degree Prior