In the context of global climate change, analyzing the driving factors of key ocean carbon cycle parameters is crucial for accurately quantifying ocean carbon sink capacity. The Convergent Cross-Mapping (CCM) method provides an effective approach for causal inference in nonlinear systems, yet it cannot characterize the ubiquitous time-lag effects within ocean carbon cycle systems, which readily results in underestimated causal strength and misidentified causal relationships. To address this limitation, this study incorporates the causal time-lag parameter (<em>Lc</em>) into the CCM framework as an optimized variable, and proposes a Lag-Convergent Cross Mapping (L-CCM) approach that accounts for optimal causal time lags, thereby constructing a time-lag embedded causal inference model. Taking the subtropical Northwest Pacific as the study domain, this study combines monthly causal screening and daily quantitative time-lag analysis to identify 18 potential drivers of sea surface <em>p</em>CO₂. Results show that L-CCM detects optimal causal time lags, under which the average causal strength of all drivers increased by 10.32 %. Strong causal drivers of sea surface <em>p</em>CO₂ include sea surface temperature (SST), sea surface salinity (SSS), chlorophyll a concentration (Chl), pH, etc., whereas weak causal factors cover surface zonal and meridional currents (<em>U</em>o, <em>V</em>o), zonal and meridional geostrophic currents (<em>U</em>gos, <em>V</em>gos). This method effectively reduces underestimation and misidentification of causal links, offers a reliable framework for ocean carbon cycle causal analysis, and holds promise for wider applications.