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Updated: Mar 18, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

Modeling confounding by half-sibling regression.

Bernhard Schölkopf¹, David W Hogg², Dun Wang²

¹Department of Empirical Inference, MPI for Intelligent Systems, Max Planck Institute for Intelligent Systems, 72076 Tuebingen, Germany; bs@tuebingen.mpg.de.

Proceedings of the National Academy of Sciences of the United States of America

|July 7, 2016

Summary

This summary is machine-generated.

We introduce half-sibling regression, a novel method to remove confounding effects and uncover latent variables. This approach, grounded in causal inference, shows promise in complex data analysis, including astronomy.

Keywords:

astronomy causal inference exoplanet detection machine learning systematic error modeling

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Area of Science:

Statistics
Causal Inference
Astronomy

Background:

Confounding variables obscure true relationships in data.
Reconstructing latent quantities is crucial for scientific discovery.
Additive noise models offer a framework for causal discovery.

Purpose of the Study:

To present a new statistical method, half-sibling regression, for confounder adjustment.
To provide theoretical underpinnings for the method with diverse data types.
To demonstrate the method's utility in a practical, challenging application.

Main Methods:

Developed the half-sibling regression technique based on causal inference principles.
Provided theoretical justification for independent and identically distributed data.
Provided theoretical justification for time series data.
Applied the method to an astronomy dataset.

Main Results:

Successfully removed confounding effects to reveal a latent quantity of interest.
Demonstrated the method's effectiveness in a complex astronomical context.
Validated the theoretical framework for both IID and time series data.

Conclusions:

Half-sibling regression is a powerful tool for confounder adjustment.
The method offers a robust way to reconstruct latent variables.
This technique has significant potential for various scientific fields, especially astronomy.