Causal Analysis and Inference

Catalog description

Causal Analysis and Inference (PAF 9272) is meant for those interested in becoming analysts, researchers, or making quantitative data analysis an important element in their careers. It teaches students how to critically evaluate existing causal analyses of both qualitative and quantitative data and how to conduct statistical analyses to answer causal questions for domestic and international policy and practice. PAF 9272 emphasizes observational and experimental data from representative surveys and requires students to write programs (coding) to carry out statistical analyses using advanced statistical software such as Stata or R. The course provides a hands-on introduction to understanding causal evidence, covering logic models and mechanisms, case-oriented vs. variable-oriented approaches, correlation vs. causation, observational vs. (quasi) experimental data, treatment effect, confounding and omitted variable bias, complex survey sampling, generalizability, standard error, confidence interval estimation, hypothesis testing, statistical and practical significance, power analysis, multiple regression, and difference-in-differences estimation. Course sections will use applications tailored towards students’ interests and concentrations (e.g., sections more populated with MIA students will have a greater international focus). Students who took PAF 9172 may take this course with the permission of instructor and if they wish to take PAF 9177. Open to Austin W. Marxe School of Public and International Affairs MPA and MIA students; others with Marxe School permission.