This course teaches classical and recent applied econometric methods in empirical microeconomics for Ph.D. students. The learning goal of this course is to be able to understand, design, and implement effective empirical strategies to support empirical claims, particularly causal claims, at the level required for professional researchers of empirical microeconomics. These empirical strategies include randomized experiments with and without compliance, regression discontinuity, the difference-in-difference, and recent techniques such as causal machine learning. In addition, I expect students to understand how to write code in R to perform simulation, estimation, and inference. Finally, I also expect students to learn how to read empirical microeconomics papers critically.