**Click here to read the chapter (link works only for UC affiliates)**

**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)