Understanding real-world 3D point clouds is challenging due to domain shifts, causing geometric variations like density changes, noise, and occlusions. The key challenge is disentangling domain-invariant semantics from domain-specific geometric variations, as point clouds exhibit local inconsistency and global redundancy, making direct alignment ineffective. To address this, we propose CounterPC, a counterfactual intervention-based domain adaptation framework, which formulates domain adaptation within a causal latent space, identifying category-discriminative features entangled with intra-class geometric variation confounders. Through counterfactual interventions, we generate counterfactual target samples that retain domain-specific characteristics while improving class separation, mitigating domain bias for optimal feature transfer. To achieve this, we introduce two key modules: i) Joint Distribution Alignment, which leverages 3D foundation models (3D-FMs) and a self-supervised autoregressive generative prediction task to unify feature alignment, and ii) Counterfactual Feature Realignment, which employs Optimal Transport (OT) to align category-relevant and category-irrelevant feature distributions, ensuring robust sample-level adaptation while preserving domain and category properties. CounterPC outperforms state-of-the-art methods on PointDA and GraspNetPC-10, achieving accuracy improvements of 4.7 and 3.6, respectively. Code and pre-trained weights will be publicly released.