Latest Articles for Observational Data

A look into causal relationships and their importance in various fields.

Researchers develop a new way to analyze gravity-mode pulsators for better stellar insights.

Investigating the impact of satellite signals on radio astronomy data.

This study examines how stellar feedback affects the evolution of dwarf spheroidal galaxies.

Examining the role of deep learning in enhancing causal models.

An overview of magnetic white dwarfs and their unique characteristics.

A look at how calibrated sensitivity models improve causal inference in studies.

Explore how genetics helps clarify health outcomes in research.

PairNet improves accuracy in estimating treatment effects using observational data.

Understanding the complexities of evaluating single-treatment effects in factorial designs.

Investigating the role and properties of neutron stars in our universe.

New insights reveal reionization as a complex process, reshaping our view of cosmic history.

A look at new techniques for identifying cause-and-effect in various fields.

A new method improves wind predictions using statistical models and data integration.

Exploring alternatives to inflation in our understanding of the universe's early stages.

This new model improves diffusion predictions and analysis through energy dynamics.

Discover how quantum non-classicality challenges our understanding of causality.

Researchers improve eclipse mapping methods for better insights into exoplanet atmospheres.

A new method enhances accuracy of treatment effects across diverse patient groups.

A look at methods for understanding long-term effects across different fields.

Research reveals how aging affects open clusters' vertical distribution in our galaxy.

This article explores a local model for studying gas clouds during star formation.

Learn how merging data types enhances research accuracy across various fields.

Investigating how chameleon fields influence white dwarfs and our understanding of gravity.

Examining inflation in the early universe through modified gravity and observational data.

Studying galaxy clusters reveals key aspects of the universe's formation.

Introducing a flexible approach to understand relationships using observational data.

Using unstructured data to estimate treatment effects more efficiently.

A detailed look at line intensity mapping and its impact on galaxy evolution.

A new method improves identification of control variables in causal studies.

Study reveals insights on metal-poor stars and their influence on galaxy formation.

A new method estimates causal effects using few interventions even with hidden factors.

This study uses machine learning to identify stars in clusters accurately.

TOI-2533 b reveals new insights on brown dwarfs and star formation.

Examining black hole collisions and the gravitational waves they create.

New methods reveal insights into protoplanets and their role in forming planets.

Using causal ML can significantly improve agricultural decision-making and sustainability.

Researchers analyze KIC4937011 to reveal insights about stellar evolution and mass loss.

A novel approach reveals diverse patient groups based on treatment outcomes.

A look at how scientists study the movement of stars in galaxies.