MORE@DIAG Seminar: Causal inference from observational data: concepts and recent methodological advances
A fundamental task of many disciplines, including economics, is to identify causal relationships and use them for explanation or for predicting the effects of policy interventions. A traditional way to discover causal relationships is to use randomized experiments. But in many cases they are too expensive, impractical or impossible to conduct. It is then necessary to infer causal relations from statistical properties of purely observational data. This seminar aims to give an introduction and a brief review of the algorithms for causal discovery that were developed in the past three decades, with a focus on methods based on graphical models and on independent component analysis. The presentation will be supplemented by some illustrations and applications to time series economic data.
Alessio Moneta is associate professor of Economics at the Institute of Economics, Scuola Superiore Sant'Anna, Pisa. His research interests lie on causal inference in econometrics, model validation, applied macroeconomics, and methodology of economics. His updated cv can be found at: https://mail.sssup.it/~amoneta/cv_am_sssup.pdf
The seminar will be online on Zoom: https://uniroma1.zoom.us/j/84687230068
ID riunione: 846 8723 0068