This intensive course provides a survey of empirical methods used for causal inference in economics research (e.g. whether our estimates will deliver answers to questions like: "what is the effect of a marketing program on sales?"). We cover a variety of research designs, statistical concept and data analytic methods for causal inference, including counterfactual, randomised experiments, treatment effects, matching, regression, difference-in-differences, panel data estimation etc. We analyse the strengths and weaknesses of these methods, as well as introduce the application of the pertinent methods using the programming language of R. The emphasis will be on understanding what various methods do (e.g. the intuition behind each method), rather than on the study of the mechanical and mathematical features of estimators. Real life examples are drawn from various fields in social science, including economics, public policy, and political science to help strengthening our understanding of these methods.