This course unit aims to provide students with a unified framework and a set of tools and strategies to estimate causal effects, and particularly the impact of policies and interventions. A large focus will be given to the use of a statistical software and to autonomous work in the analysis of data sets.
The course unit will cover the following topics:
• Definition of causal effects
• Evaluation of causal effects in an experimental and in an observational setting
• The problem of selection
• Identification of causal effects by means of regression analysis and matching techniques, instrumental variables, difference in differences methods, regression discontinuity, synthetic control
• The relationship between machine learning and causal inference.
The course unit will cover the following topics:
• Definition of causal effects
• Evaluation of causal effects in an experimental and in an observational setting
• The problem of selection
• Identification of causal effects by means of regression analysis and matching techniques, instrumental variables, difference in differences methods, regression discontinuity, synthetic control
• The relationship between machine learning and causal inference.
- Teacher: Lorenzo Rocco