Instrumental Variable (IV)
A variable that influences the treatment variable but affects the outcome only through the treatment – not directly. Enables causal estimates despite confounding.
Instrumental Variables enable causal estimates despite confounding – powerful, but finding good instruments is econometrics' greatest challenge.
Explanation
Two conditions: (1) Relevance: The instrument correlates with the treatment. (2) Exogeneity: The instrument affects the outcome ONLY through the treatment. 2SLS (Two-Stage Least Squares) is the standard estimation method.
Marketing Relevance
Solves the fundamental problem: "How do we measure the effect of X on Y when we cannot randomize and confounders exist?"
Common Pitfalls
Good instruments are extremely hard to find. Weak instruments produce biased estimates. Exclusion restriction is untestable.
Origin & History
Philip Wright introduced IVs in 1928. Angrist & Imbens formalized LATE (Local Average Treatment Effect) and received the 2021 Nobel Prize. IVs are the backbone of modern econometrics.
Comparisons & Differences
Instrumental Variable (IV) vs. Difference-in-Differences
DiD uses parallel trends; IV uses an exogenous instrument. Different assumptions, different settings.
Instrumental Variable (IV) vs. Randomized Experiment
Randomization eliminates all confounders; IVs address confounding only for the variation induced by the instrument.