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- Explain how correlation differs from causation in regression models
- Learn the three sources of the endogeneity problem and how they cause assumption CR5 to fail
- Learn about some solutions to the endogeneity problem
What We Learned
- Correlation means that if you tell me X, I can make a prediction of Y.
- Causation means that if you change X to a different value, then I expect Y to change.
- The three sources of “endogeneity” are:
- Measurement error in X variables usually (but not always) leads to coefficient estimates that are smaller than they should be (biased toward zero). Proxy variables can help reduce measurement error bias.
- Simultaneity means that X and Y cause each other.
- Omitted variables mean that you may attribute the causal effect of one variable to another
- Fixed-effects estimation can mitigate the omitted variables problem in panel data (but only for time-invariant omitted variables)