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October 2, 2025Open Access

Deep learning based doubly robust test for Granger causality

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Proposed doubly robust Granger causality test improves time series analysis, indicating broader deep learning applications.

The doubly robust Granger causality test efficiently handles large lag orders, improving time series analysis.
Test asymptotically controls type I error with a power approaching one, validated through numerical simulations.
A multiplier bootstrap method efficiently obtains critical values while reducing computational time.
Application of DRGCT reveals insights into the price-volume relationship in stock markets across America, China, and Japan.

Hui et al. (2025) studied this question.

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