Causal Inference and Model Selection in Econometrics


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


• The second part applies the model

and discusses when and how can we

make causal inference using

regression analysis.


• 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


January 09th~12th, 2018

13:20 ~ 16:30


Chungsang Tom Lam, Clemson University


Room 102, School of Information Management & Engineering, Shanghai University of Finance & Economics