Berkeley Methods Workshop, 2014
This week-long course offers an applied introduction to causal inference for social scientists. The course is structured around four key topics: Modern causal notation (potential outcomes and directed acyclic graphs [DAGs]), propensity score matching, instrumental variables, and inference for time-varying treatments. Subsidiary topics include the art of formulating causal questions, the mechanics of balance testing, sensitivity analysis, effect heterogeneity, social networks, and causal mediation analysis. Throughout, the course will focus on building strong transferable intuition. We will prioritize a thorough substantively grounded understanding of assumptions over mathematical proofs and derivations. The goal is to empower applied social scientists to apply new causal tools with confidence.
Participants should have a solid applied background in multiple regression. The course does not require calculus or matrix algebra, though neither will hurt. We will execute empirical examples in Stata.
Fee: $1500 (USD) Registrants will be contacted for payment options
UC Berkeley Graduate Students may apply for a stipend. Contact Jon Stiles at firstname.lastname@example.org