This course provides an introduction to causal inference and model selection using econometric analysis. 

The class is roughly divided into two parts. 

•  The first part introduces the basics of

   the linear regression model and its 

   limitation. 

• The second part applies the model 

   and discusses when and how can we 

   make causal inference using

   regression analysis.

Topics

•  Sample Mean and the Concept of  Averages

•  Linear Regression Basics

•  Omitted Variable Bias and Bad Control

•  Instrumental Variables and Two-stage Least Squares

•  Average Treatment Effects and Local Average Treatment 

•  Fixed Effects and Random Effects

•  Generalised Least Squares and Feasible Generalised Least Squares

•  Robust Standard Errors

•  Difference in Differences

•  Regression Discontinuity

•  Bootstrapping