← Back to papers
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
🧭 Keyword Pioneer — confounder removal
🐣 Hot Topic Early Bird — causal inference
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

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

Related papers

PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data 2015
The Benefits of Learning with Strongly Convex Approximate Inference 2015
Tracking Approximate Solutions of Parameterized Optimization Problems over Multi-Dimensional (Hyper-)Parameter Domains 2015
Distributed Estimation of Generalized Matrix Rank: Efficient Algorithms and Lower Bounds 2015
Unsupervised Riemannian Metric Learning for Histograms Using Aitchison Transformations 2015