AI methods powered by Deep Neural Networks (DNN) are so successful that some studies even claim they have surpassed human performance. Their complex black-box nature, lack of generalization out-of-distribution (OOD) and mistaken correlation for causation, which can have catastrophic consequences for data-driven decision-making limit their trustability and social acceptance.This has given rise to the quest for AI model robustness, interpretability, and explainability. Explainable AI (XAI) is a hot topic in the field of ML, which has even become a political and legal concern. Indeed, with the increasing use of AI in systems governing our society, important decisions based on the DNNs' predictions can be made with a great impact on human life. To ensure the AI systems stay in phasis with social interests, it is crucial to becoming computed accurate predictions trustable by empowering the AI model used by the decision maker with robustness, transparency, and explainability. XAI has a panel of goals; trustworthiness is the main and, one of which is causality. Answering causal questions from data is an open avenue to XAI. Causality offers an unexplored complement to ML, allowing it to go beyond model associations. Learning a causal model provides the mechanisms giving rise to the observed statistical dependencies, and allows both to model distribution shifts through interventions, and to raise the veil on the model process leading to a prediction. Thus, the OOD generalization is not limited to predictive performance or robustness but also the explainability and reliability of the model process across switching distributions. All these aspects make Causality for XAI and generalization a key concern. This project aims to develop an approach that benefits from recent advances in causality for tackling modern ML problems such as XAI and generalization for trustable informing decision-making process.