<p>Causal inference for spatial cross-sectional data is an essential approach to reveal the driving mechanisms of geographical phenomena. As an emerging method for causal inference with spatial cross-sectional data, Geographical Convergent Cross Mapping (GCCM) is easily disturbed by “false neighbors” caused by spatial autocorrelation, and this can lead to misjudgment of causal direction or overestimation of causal strength. In view of this problem, this paper proposes a Geographical Convergent Cross Mapping method accounting for Spatial Autocorrelation (SA-GCCM). The method introduces spatial path roughness to measure the smoothness of attribute value changes between two points, and constructs an iterative neighbor screening mechanism to eliminate the “false neighbors” problem caused by spatial autocorrelation. In addition, the proposed method identifies true causal relationships by examining how the predictive performance changes with the spatial path roughness. To verify the effectiveness of the SA-GCCM method, this study conducted experiments on two typical datasets with clear causal relationships: nighttime-light data and topographic factors, and net primary productivity and climatic factors. The experimental results show that SA-GCCM can effectively address the issues of causal direction misjudgment and causal strength overestimation in the original GCCM method. This study not only improves the robustness of the GCCM method, but also provides methodological support for handling geographical data with spatial autocorrelation.</p>