Causal inference is considered a crucial topic in the medical field, as it enables the determination of causal effects for medical treatments through data analysis. However, the vast volume and complexity of medical data present significant challenges for traditional machine learning methods in accurately assessing treatment effects. Issues such as noise in the data, unstructured information, and label sparsity can lead to unstable causal identification and erroneous correlation inference. To address these challenges, we propose a systematic survey of causal inference in the medical field, which encompasses studies utilizing observational data, aimed at organizing and summarizing the key concepts, methods, and applications of causal inference. Moreover, the causal inference applications are presented across various types of medical data, including medical images and Electronic Medical Records (EMR), using specific medical cases as examples. The thorough review not only emphasizes the theoretical and practical significance of causal inference methods but also highlights potential research directions in the medical domain.