This course offers an advanced treatment of design issues in social and political research that aims at causal inference, that is, at answering cause-and-effect questions of the general form: is X a cause of Y? If so, how large is the causal effect of X on Y?
Starting from an exposition of the counterfactual model of causality and causal graphs, the course introduces the assumptions necessary for identifying causal effects, and shows how and to what extent those assumptions are justified in a various experimental and observational research designs. The course gives an overview of common and new empirical strategies for causal inference, such as regression and panel estimators, matching, instrumental variable, regression discontinuity, and synthetic control approaches.
As to observational studies, the course gives an overview of common and new large-N methods for causal inference, such as regression and panel estimators, matching, instrumental variable and regression discontinuity approaches. The course also discusses how the principles and methods introduced may be put to use in small-N settings and in studies which aim to parse the mechanisms underlying causal effects.
The course’s primary aim is to provide students with the epistemological and methodological tools to critically evaluate existing empirical studies, to identify their inferential weaknesses, and to develop research designs on their own that, to the greatest possible extent, respond to these problems.
The course takes place on Tuesday 11:45-13:15 in R-712.