Course: Advances in Causal Inference II
Professor: Andrea Albanese
ECTS: 1
Aims:
The aim of this course is to cover the latest developments in the methods of difference-in-differences (DID) literature.
The course will provide a brief overview of the traditional DID and its limitations under settings where adoption of treatment is staggered and will discuss the different solutions that have been proposed based on both parametric and semiparametric methods, controlling for differential trend on the observables and allowing for heterogeneity of treatment effects.
Other advancement of the literature will be also covered such as scenarios with treatment exits, trend adjustments, continuous treatment, outcome transformations, non-linear models, and issues related to pre-trends pretesting.
Throughout the course, hands-on implementation of the methods with STATA will be provided, which would enable students to apply these methods effectively to real-world datasets and empirical research questions. The course builds on the Advances on Causal Inference I course, which covers the identification of treatment effects under heterogeneity, which is a prerequisite.
Learning Objectives:
- Gain familiarity with the latest developments in causal inference techniques, with a focus on addressing heterogeneity in treatment effects.
- Identify strengths, limitations, and potential biases in research designs and methodologies.
- Demonstrate the application of learned techniques to actual datasets and real-world research questions.
- Develop problem-solving skills for tackling complex econometric challenges and making informed decisions about appropriate methods.
