Causal inference has numerous real-world applications in many domains such as education, health care, political science, marketing, and online advertising. Causal inference has been studied for decades, however, traditional methods may have limited capability on handling large-scale and high-dimensional heterogeneous data. Recent research efforts demonstrate that machine learning could greatly facilitate causal inference tasks such as treatment effect estimation and counterfactual inference. Meanwhile, casual knowledge extracted from observational data could enhance the reliability of machine learning models. In this tutorial, we will present a comprehensive overview of the intersection of causal inference and machine learning. We will start with the background of causal inference and briefly introduce several traditional causal inference methods. Then we will introduce the state-of-the-art machine learning algorithms for causal inference, especially the representation learning based methods and graph neural networks based methods. In addition, we will discuss how to exploit causal knowledge for trustworthy machine learning, such as explanation, fairness, robustness, and so on. Finally, we showcase promising industrial applications of these methods in multiple domains.