In this course, we cover selective concepts in causal inference and cutting-edge research topics. In most causal models, minimal parametric assumptions or identification restrictions based on the targeted causal estimands are made, with the remaining distributional assumptions for the observed and unobserved variables left as unspecified/unrestricted. To understand the properties of causal estimates, the semi-parametric inference framework is also introduced for model-based, assumption-lean and model-free analyses.