Detecting Causality by Combined Use of Multiple Methods: Climate and Brain Examples.

Area of Science:

• Complex Systems Analysis
• Causal Inference
• Time Series Analysis

Background:

• Understanding complex system behavior requires identifying causal relations from time series data.
• Existing methods often struggle with nonlinear dynamics and unobserved common causes in coupled systems.

Purpose of the Study:

• To develop and validate a robust method for inferring causality in complex systems using time series data.
• To address limitations of existing methods by combining multiple techniques and employing a majority vote.

Main Methods:

• Proposed a novel ensemble approach combining three causality detection methods with a majority vote.
• Developed two new causality detection methods applicable to nonlinear dynamics and hidden common causes.
• Validated the ensemble method using simulated data from coupled logistic, Rössler, and Lorenz models.

Main Results:

• Successfully inferred causal relationships in coupled nonlinear systems, including those with unobserved components.
• Demonstrated the method's efficacy on real-world data, such as ice core records (temperature, CH4, CO2) over 800,000 years.
• Applied the method to analyze brain region interactions during a motor task, revealing distinct causal influences over time.

Conclusions:

• The combined use of multiple causality detection methods with a majority vote significantly enhances the reliability of causal inference from time series data.
• This ensemble approach is effective for analyzing complex nonlinear systems, including those with hidden confounders and irregularly sampled data.
• The validated method provides robust insights into causal interactions in diverse scientific domains, from paleoclimatology to neuroscience.