# Removing systematic errors for exoplanet search via latent causes

arXiv: https://arxiv.org/abs/1505.03036 · HTML: https://mlsanity.com/paper/1505.03036/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 May 2015.

## Built on by (most cited first, 4 of 4)

- [Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation](https://mlsanity.com/paper/2103.00136/) (2021)
- [Management Decisions in Manufacturing using Causal Machine Learning -- To Rework, or not to Rework?](https://mlsanity.com/paper/2406.11308/) (2024)
- [Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning](https://mlsanity.com/paper/2412.02439/) (2024)
- [Sibling Regression for Generalized Linear Models](https://mlsanity.com/paper/2107.01338/) (2021)

## 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.