Assumptions in Causal Inference adopts a first-principles approach to examining assumptions in causal inference, breaking down complex problems into fundamental building blocks. The structure of this monograph follows a logical progression from the establishment of foundational concepts to the application of them in practice. To achieve this, Section 2 introduces the causal languages that will be used throughout the monograph, specifically the potential outcome framework and the causal graph perspective. Section 3 establishes the centrality of assumptions in causal inference by systematically exploring causal identification and presenting three general identification strategies that underpin most research designs. Section 4 deepens this discussion by focusing on understanding assumptions, drawing from the philosophy of science, and illustrating their role in commonly used empirical designs such as matching, difference-in-differences (DID), and regression discontinuity design (RDD). Building on this foundation, Section 5 shifts to the assessment of assumptions, introducing key methods such as placebo (falsification) tests, consistency tests, and sensitivity analysis to evaluate the credibility of assumptions in empirical research. Finally, Section 6 presents a systematic framework for dealing with assumptions, with a particular emphasis on relaxing unrealistic assumptions to enhance the credibility of causal inference.