Figure 2 (IMAGE)
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Comparison of causal inference results between the developed methodology (GOBI) and existing model-free methods (GC: Granger Causality; CCM: Convergent Cross Mapping; and PCM: Partial Cross Mapping). (a) When combining two unrelated time-series data from prey-predator systems (P and D) and intracellular protein interaction systems (σ^28 and TetR), existing methodologies such as GC and CCM tend to erroneously infer causal relationships between nearly all components when there is synchrony in the time series data. However, GOBI accurately infers only causal relationships that actually exist. (b) Time-series data presents the hospital admissions for cardiovascular diseases and concentrations of air pollutants in Hong Kong. Unlike other methodologies, GOBI correctly identifies that only nitrogen dioxide (NO2) and respirable suspended particulate (Rspar) have an impact on cardiovascular disease, regardless of the length of the data used (2 or 3 years).
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