Extensions
Introduction to Machine Learning for Economists (Under Construction)
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Welcome
Preface
Basics
1
Non-linear function estimation
2
Bias-variance Trade-off
3
Cross-validation
4
Regression Shrinkage Methods
5
Bootstrap
Prediction ML
6
Random Forest
7
Boosted Regression Forest
8
Extreme Gradient Boosting
9
Local Linear Forest
Causal Machine Learning (CML) Methods
10
Why can’t we just do this?
11
Double Machine Learning
12
S-, X-, T-, and R-learner
13
Forest-based CATE Estimators
14
Extensions of Causal Forest
15
Causal Model Selection
Extensions
16
Spatial Cross-validation
17
Generalized Random Forest
Programming: R
18
Machine Learning with
mlr3
19
Running Python from R
20
Model Selection (Prediction)
Programming: Python
21
Prediction
22
Treatment Effect Estimation
23
Model selection
Appendices
A
Monte Carlo (MC) Simulation
B
Primer on method of moment
Extensions
Code
15
Causal Model Selection
16
Spatial Cross-validation
Source Code
# Extensions