Explainability plays a critical role in understanding the workings of Graph Neural Networks (GNNs). While recent methods have introduced causal inference into GNN explanation, they predominantly rely on individual-level interventions and lack rigorous statistical causality testing, resulting in unfaithful and unreliable explanations. To address these challenges, we propose CastX that integrates cohort-level causal analysis with statistical causality testing for GNN explanations. Specifically, CastX formulates the discovery of explanatory subgraphs as a dynamic edge pruning task guided by Conditional Average Treatment Effect (CATE) estimation. A reinforcement learning agent is employed to iteratively eliminate spurious edges and identify causally informative substructures. To further enhance reliability, we introduce an i.i.d.-agnostic non-parametric permutation test that assesses the statistical significance of each target edge. Extensive experiments on real-world datasets demonstrate that our CastX outperforms existing methods in yielding explanatory subgraphs that are concise, faithful, reliable, and statistically supported.