Data/Inference for Policymkers

3 Credits · Level: Graduate, Undergraduate

Data and Inference for Policymakers Policymakers hear and make causal claims all the time: o “Conditional cash transfers reduce short- and long-term poverty” o “Stay-at-home policies prevented the spread of Covid-19” o “Community policing initiatives increase trust in local government” How do we assess the validity of those claims? In this course, you will learn to “think with data.” The course will provide you with three core skills: basic competency in data manipulation and visualization (using R), intro-level familiarity with probability and statistics, and counterfactual thinking/analysis. The course assumes little or no background in statistics or quantitative analysis, but is aimed at training policymakers who are confident consuming, evaluating, and drawing conclusions from research. Because these skills are likely new to the students, they require significant practice. The first half of each class (about 1 hour) will provide theoretical background on key concepts, mostly through lecture. The second half of the class will be devoted to developing the inferential toolkit through partnered practice. The problem sets aim to link these two skills by having students work through applied examples from the policy world. Because the course is housed in Latin American Studies, these examples will come primarily from that region, but the lessons and tools are broadly applicable. PRE-REQUISITES There are no prerequisites for masters students to take this class. If you are an undergraduate interested in taking this class, you must contact the department for approval.