Causal Learning through Deliberate Undersampling.

Area of Science:

• Causal inference
• Dynamical systems analysis
• Time series analysis

Background:

• Scientists often face limitations in measurement frequency for social, physical, and biological systems.
• A prevailing assumption is that higher measurement frequencies are necessary for richer data on causal structures.

Purpose of the Study:

• To challenge the assumption that higher measurement frequencies are always optimal for causal inference.
• To demonstrate that measuring systems more slowly can yield additional information about causal structure.
• To present a novel algorithm for inferring causal structure using data from multiple timescales.

Main Methods:

• Development of an algorithm that utilizes graphical representations across multiple measurement timescales.
• Inference of underlying causal structure by incorporating data from slower measurement frequencies.
• Simulation studies to evaluate the probability and magnitude of gains from deliberate undersampling.

Main Results:

• Demonstration that measuring systems more slowly can, in certain situations, provide more informative data on causal structure.
• Inclusion of slower timescale structures can reduce the ambiguity (equivalence class size) of possible causal models.
• Simulation data quantifies the conditions and extent to which undersampling improves causal inference.

Conclusions:

• The assumption that faster measurement is always superior for understanding causal mechanisms is flawed.
• Incorporating data from slower timescales offers a viable strategy for enhancing causal structure inference.
• The developed algorithm provides a practical method for leveraging multi-timescale data to improve scientific understanding of complex systems.