Interactive Causal Structure Discovery in Earth System Sciences

TL;DR

This paper introduces an interactive workflow for causal structure discovery in Earth system sciences that incorporates domain knowledge, enabling more accurate and usable causal models through user-guided modifications.

Contribution

It presents a novel interactive approach to causal discovery that models user input as a likelihood maximization problem, addressing the integration of expert knowledge into causal models.

Findings

Interactive modification improves model relevance

Domain knowledge significantly influences causal model outcomes

Open research questions remain for further refinement

Abstract

Causal structure discovery (CSD) models are making inroads into several domains, including Earth system sciences. Their widespread adaptation is however hampered by the fact that the resulting models often do not take into account the domain knowledge of the experts and that it is often necessary to modify the resulting models iteratively. We present a workflow that is required to take this knowledge into account and to apply CSD algorithms in Earth system sciences. At the same time, we describe open research questions that still need to be addressed. We present a way to interactively modify the outputs of the CSD algorithms and argue that the user interaction can be modelled as a greedy finding of the local maximum-a-posteriori solution of the likelihood function, which is composed of the likelihood of the causal model and the prior distribution representing the knowledge of the expert user. We use a real-world data set for examples constructed in collaboration with our co-authors, who are the domain area experts. We show that finding maximally usable causal models in the Earth system sciences or other similar domains is a difficult task which contains many interesting open research questions. We argue that taking the domain knowledge into account has a substantial effect on the final causal models discovered.