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This research aims to evaluate the effectiveness of various causal inference methods for analyzing climate tipping elements.

Quantitative comparison of causal inference methods for climate tipping points

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Randomized trial compares methods for studying climate tipping points, indicating unique advantages for each approach.

This research aims to evaluate the effectiveness of various causal inference methods for analyzing climate tipping elements.
Quantified accuracy of three causal inference methods: LKIF, PCMCI, and Granger causality.
Applied LKIF and PCMCI methods to reanalysis data for detecting tipping point interactions.
Assessed the importance of aligning data sampling intervals with interaction delays and including confounders.
LKIF is preferred for limited data settings, while PCMCI allows for expert knowledge integration.
Granger causality is beneficial for large datasets and captures delayed interactions effectively.
Identified bidirectional stabilizing interactions between the AMOC and Arctic summer sea ice from method applications.

Lohmann et al. (2026) studied this question.

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