# Chapter 11 - Identifying Causation

Lecture Slides:      Powerpoint     PDF

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

Examples

• 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)