Causal Methods Comp Social Sci

3 Credits · Level: Graduate

Causal Inference for Computational Social Science This course provides students with the opportunity to take the analytical skills, machine learning algorithms, and statistical methods learned throughout their first year in the program and explore how they can be employed towards carrying out rigorous, original research in the behavioral and social sciences. With a particular emphasis on tackling the additional challenges which arise when moving from associational to causal inference, particularly when only observational (as opposed to experimental) data is available, students will become proficient in cutting-edge causal Machine Learning techniques such as propensity score matching, synthetic controls, causal program evaluation, inverse social welfare function estimation from panel data, and Double-Debiased Machine Learning. In-class examples will cover continuous, discrete-choice, and textual data from a wide swath of social and behavioral sciences: economics, political science, sociology, anthropology, quantitative history, and digital humanities. After gaining experience through in-class labs and homework assignments focused on reproducing key findings from recent journal articles in each of these disciplines, students will spend the final weeks of the course on a final project demonstrating their ability to develop, evaluate, and test the robustness of a causal hypothesis.