Encoding Causal Macrovariables

TL;DR

This paper presents a new algorithm for automatically identifying macrovariables that serve as causal bottlenecks in complex systems, demonstrated on climate data and synthetic datasets.

Contribution

It introduces a novel information-theoretic approach to detect causal macrovariables and their relationships, adaptable to different scientific contexts.

Findings

Successfully detects ground-truth variables in synthetic data.

Robustly identifies El Nino related variables in climate data.

Provides a flexible framework for causal macrovariable detection.

Abstract

In many scientific disciplines, coarse-grained causal models are used to explain and predict the dynamics of more fine-grained systems. Naturally, such models require appropriate macrovariables. Automated procedures to detect suitable variables would be useful to leverage increasingly available high-dimensional observational datasets. This work introduces a novel algorithmic approach that is inspired by a new characterisation of causal macrovariables as information bottlenecks between microstates. Its general form can be adapted to address individual needs of different scientific goals. After a further transformation step, the causal relationships between learned variables can be investigated through additive noise models. Experiments on both simulated data and on a real climate dataset are reported. In a synthetic dataset, the algorithm robustly detects the ground-truth variables and correctly infers the causal relationships between them. In a real climate dataset, the algorithm robustly detects two variables that correspond to the two known variations of the El Nino phenomenon.