Causal Inference

What is the effect of smoking on health? Does having an additional child increase the risk of poverty? Are development policies targeted on small firms effective in increasing investments? Most studies in the social sciences are motivated by questions that are causal in nature. However, in these areas experiments are not always possible because of ethical or practical reasons and the estimation of causal effects has often to rely on observational studies. The validity of inference will then strictly depend on the plausibility of the assumptions underlying the employed statistical techniques. This course will cover some of the most popular techniques for estimating causal effects with observational data: propensity score matching, instrumental variable regression, regression discontinuity designs and fixed effects models. Special emphasis will be placed during the course on discussing the plausibility of the identifying assumptions, the data requirements and other practical and theoretical challenges for the implementation of each method. This short course will offer participants theoretical and applied perspectives on the covered topics. Examples will be drawn from political science, sociology, economics, public health and policy evaluation. Lab sessions will demonstrate the implementation of the covered techniques using the software Stata. References to examples in R will be also provided.

Course leader

Bruno Arpino, PhD - University of Florence

Target group

Professionals, Researchers, and Students, who wish to develop high-quality surveys and employ up-to-date statistical methods for survey data analysis. Potential attendees include survey practitioners, marketing professionals, social science students, and researchers.