Removing systematic errors for exoplanet search via latent causes
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2015.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 2.
4 papers.
- 2021Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentationcited by 10
- 2024Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?cited by 1
- 2024Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learningcited by 1
- 2021Sibling Regression for Generalized Linear Modelscited by 0
- 2024Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning3 Dec 2024
- 2024Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?17 Jun 2024
- 2021Sibling Regression for Generalized Linear Models3 Jul 2021
- 2021Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation27 Feb 2021