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Updated A.Y. 2022-2023

Class website: Microsoft Teams, code: wtokndf

This module provides an introduction to the tools used in Applied Microeconomics to study causal inference. We will cover the conceptual basis of the methods in an intuitive way, and you will learn how to apply and code such methods using the free software R. Coding in R will be an important component of the module. We will use real-life datasets from recent empirical papers.

After a short introduction to the Potential Outcomes framework of causality, the methods that we will cover include Randomized Controlled Trials, Difference-in-Differences, Regression Discontinuity, and (time permitting) Instrumental Variables. The programming part of the module in R will cover recent academic papers using these methods.


In order to follow the course, pre-existing knowledge of the following is desirable:

  • Basic probability and statistics
  • Linear Regression Model and OLS
  • Some familiarity with coding (although not strictly necessary), ideally in R


We will not follow closely any textbook in particular, but the following books are highly recommended since they provide an introductory exposition of the topics we will cover:

  • "Causal Inference: The Mixtape" (2021). Author: Scott Cunningham
  • "Mastering Metrics: The Path from Cause to Effect" (2014). Authors: Joshua D. Angrist and Jorn-Stefen Pischke


There will be no exam for this module. Grades will be assigned based on a series of assignments carried out during the duration of the course. These assignments will be mostly based on data analysis using R.