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However, few existing methods attempt to capture the intuitive human understanding of causality: that an intervention on one variable leads to a subsequent change in another. To bridge this gap, this paper introduces a causal discovery method named Intervened TaylorKAN (ITK), which simulates controlled experiments within reconstructed systems. Specifically, we leverage the recently emerging Kolmogorov\u2013Arnold Networks (KANs) with Taylor series as activation functions to construct a surrogate model that faithfully replicates the original system\u2019s dynamics. The core of ITK involves applying targeted interventions on this reconstructed system and observing the responses in other variables, thereby providing more intuitive causal inferences. We evaluated ITK\u2019s performance through extensive experiments on both synthetic and real-world datasets. Results demonstrate that ITK achieves high accuracy on synthetic data with known causal structures, confirming its validity. 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