400020 SE SE Methods for Doctoral Candidates (2015W)

Do hospitals make people healthier? Is it a problem that more people die in hospitals than in bars? Does an additional year of schooling increase future earnings? Do parties that enter the parliament enjoy vote gains in subsequent elections? The answers to these questions (and many others which affect our daily life) involve the identification and measurement of causal links: an old problem in philosophy and statistics. To address this problem we either use experiments or try to mimic them by collecting information on potential factors that may affect both treatment assignment and potential outcomes. Customary ways of doing this in the past entailed the specification of sophisticated versions of multivariate regressions. However, it is by now well understood that causality can only be dealt with during the design, not during the estimation process. The goal of this workshop is to familiarize participants with the logic of casual inference, the underlying theory behind it and introduce research methods that help us approach experimental benchmarks with observational data. Hence, this will be a much applied course, which aims at providing participants with ideas for strong research designs in their own work. During the five-days of the course, participants will be introduced into an authoritative framework of causal inference in social sciences, i.e. the potential outcomes framework. Using this language, we will then delve into three design-based identification strategies:
1. Instrumental Variables;
2. Regression Discontinuity Design; and
3. Difference-in-Differences estimation.
For every method, the following structure will be employed: first, a running example from the literature will provide the motivation and intuition. We will then proceed with the formal identification derivation and finally we will focus on estimation strategies and robustness checks. For each method there will be a hands-on lab section, where we will apply these methods with real data. The last section of the course will be used to introduce advances and extensions in these methods, drawing mainly on causal mechanisms, bounding analysis and front-door-based identification strategies.