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_toc.yml
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_toc.yml
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# Table of contents
# Learn more at https://jupyterbook.org/customize/toc.html
format: jb-book
root: intro
options: # The options key will be applied to all chapters, but not sub-sections
numbered: True
chapters:
- file: Python_notebooks/01_Python_Notebook_Linear_Model_Overfitting
- file: Python_notebooks/02_ols-and-lasso-for-wage-prediction
- file: Python_notebooks/03_ols-and-lasso-for-gender-wage-gap-inference
- file: Python_notebooks/04_py-notebook-some-rct-examples
- file: Python_notebooks/05_py-notebook-analyzing-rct-with-precision
- file: Python_notebooks/06_analyzing-rct-reemployment-experiment
- file: Python_notebooks/07_py-notebook-linear-penalized-regs
- file: Python_notebooks/08_ML_for_wage_prediction
- file: Python_notebooks/09_py-notebook-experiment-on-orthogonal-learning
- file: Python_notebooks/10_double-lasso-for-the-convergence-hypothesis
- file: Python_notebooks/11_py-heterogenous-wage-effects
- file: Python_notebooks/12_py-colliderbias-hollywood
- file: Python_notebooks/13_deep_neural_networks_for_wage_prediction
- file: Python_notebooks/14_automl-for-wage-prediction
- file: Python_notebooks/15_py-Functional-Approximation-By-NN-and-RF
- file: Python_notebooks/16_notebook_dagitty
- file: Python_notebooks/17_notebook-dosearch
- file: Python_notebooks/18_pm3_notebook_inference_clustering
- file: Python_notebooks/19_pm3_notebook_inference_nn
- file: Python_notebooks/20_pm5-401k-kaggle-py
- file: Python_notebooks/21_Identification Analysis of 401(k) Example w DAGs
- file: Python_notebooks/22_debiased-ml-for-partially-linear-model-in-python
- file: Python_notebooks/23_sensitivity_analysis_with_sensmakr_and_debiased_ml
- file: Python_notebooks/24_debiased-ml-for-partially-linear-iv-model-in-python
- file: Python_notebooks/25_r_weak_iv_experiments