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Causal Inference (SPS-METRI-CAU-26)

SPS-METRI-CAU-26

Purpose

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 course 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. Particular emphasis is placed on how to use these methods in order to address historical questions. Why did Protestant countries flourish more than catholic ones? How did the European migrants integrate in the US throughout the 20th century? What is the effect of Nazi territorial control on patterns of resistance during the WWII? While discussing these and other applications, we will also shed light on how to think about archival work under the prism of causal inference. Hence, this will be a much applied course, which aims at providing participants with ideas for strong research designs in their own work.

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