Instrumental variables are a very common and well-established technique to perform causal inference from observational data. When an OLS estimation can be compromised by measurement error, omitted variable bias, or simultaneous causation issues, an instrumental variable (IV) regression can be a fitting solution. Today we are discussing the implementation of instrumental variable regression through two-stage least squares (TSLS), indirect least squares, and matrix algebra based on a famous Acemoglu, Johnson, and Robinson (2001) paper on causal effects of institutional quality on economic growth with log settler mortality as an instrument. We will also consider Excel estimation of IV regression, its assumptions and limitations, and the conceptual idea behind instrumental variables
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