The pursuit of generalizable representations remains to be a dynamic field in the realm of machine learning and computer vision. Existing methods aim to secure invariant representations by either harnessing domain expertise or leveraging data from multiple domains. In this paper, we propose a novel approach that identifies the Causal Markov Blanket (CMB) representations and improves the Out-of-distribution prediction performance. We establish a framework guided by a structural causal model (SCM) describing the data generation process, allowing for the causal Markov Blanket discovery in the latent space. We then construct an invariant prediction mechanism using CMB features, suitable for performing prediction across domains. In comparison to state-of-the-art domain generalization methods, our approach exhibits robustness and adaptability under distribution shifts.