Chapter 11 - Identifying Causation

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Learning Objectives

  • 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


  • TBA

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)