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

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