MIT's Josh Angrist introduces one of econometrics most powerful tools: instrumental variables.

Instrumental variables (IV, for those in the know), allow masters of econometrics to draw convincing causal conclusions when a treatment of interest is incompletely or imperfectly randomized.

For example, arguments over American school quality often run hot, boiling over with selection bias. See a school with strong graduation rates and enticing test scores? Is that a good school or just an ordinary school fortuitously located in a good neighborhood?

Lotteries that randomize offers of a school seat at in-demand schools should unravel the school quality conundrum. But lotteries only offer seats. Families are free to accept or go elsewhere and these choices are far from random.

IV provides a path to causal conclusions even in the face of this sort of incomplete randomization.

In this video, we cover the following:

- Incomplete random assignment

- IV terminology: first stage, second stage, instrument, reduced form

- Three key IV assumptions: substantial first stage, independence assumption, exclusion restriction

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