Instrumental Variables in Action:
Sometimes You Get What You Need
Anything that happens, happens.
Anything that, in happening, causes something else to happen,
causes something else to happen.
Anything that, in happening,
causes itself to happen again, happens again.
It doesn't necessarily do it in chronological order, though.
Douglas Adams, Mostly Harmless
Two things distinguish the discipline of econometrics from the older sister field of statistics. One is a lack of shyness about causality. Causal inference has always been the name of the game in applied econometrics. Statistician Paul Holland (1986) cautions that there can be [no causation without manipulation,] a maxim that would seem to rule out causal inference from nonexperimental data. Less thoughtful observers fall back on the truism that [correlation is not causality.] Like most people who work with data for a living, we believe that correlation can sometimes provide pretty good evidence of a causal relation, even when the variable of interest has not been manipulated by a researcher or experimenter.1
The second thing that distinguishes us from most statisticians—and indeed from most other social scientists— is an arsenal of statistical tools that grew out of early
1 Recent years have seen an increased willingness by statisticians to discuss
statistical models for observational data in an explicitly causal framework;
see, for example, Freedman's (2005) review.