Notebooks sorted

	A	B	C	D
1	Notebook	Topic	Python (work in progress, please create issues at https://github.com/CausalAIBook/MetricsMLNotebooks)	Short Description
2	CM-1	https://www.kaggle.com/victorchernozhukov/r-notebook-analyzing-rct-with-precision	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/CM1/rct_simulation.ipynb	Analyzing RCT with precision by adjusting for baseline covariates
3	CM-1	as above	https://github.com/CausalAIBook/MetricsMLNotebooks/blob/main/CM1/rct_simulation.ipynb	link in book
4	CM-1	https://www.kaggle.com/victorchernozhukov/analyzing-rct-reemployment-experiment	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/CM1/rct_penn.ipynb	Reemployment experiment
5	CM-1	as above	https://github.com/CausalAIBook/MetricsMLNotebooks/blob/main/CM1/rct_penn.ipynb	link in book
6	CM-1	https://www.kaggle.com/code/victorchernozhukov/r-notebook-some-rct-examples/notebook	https://github.com/CausalAIBook/MetricsMLNotebooks/blob/main/CM1/rct_vaccines.ipynb
7	CM-2B	https://www.kaggle.com/victorchernozhukov/r-colliderbias-hollywood	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/CM2/colliderbias-hollywood.ipynb	Collider Bias
8	CM-3	https://www.kaggle.com/victorchernozhukov/notebook-dagitty	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/CM3/dagitty.ipynb	Causal identification using DAGs
9	CM-3	https://www.kaggle.com/victorchernozhukov/notebook-dosearch	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/CM3/dosearch.ipynb	Illustration of capabilities of the "dosearch" package
10	PM-1	https://www.kaggle.com/victorchernozhukov/r-notebook-linear-model-overfiting	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/PM1/overfitting.ipynb	Overfitting example
11	PM-1	https://www.kaggle.com/janniskueck/pm1-notebook1-prediction-newdata	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/PM1/PM1_prediction.ipynb	Wage Prediction
12	PM-1	https://www.kaggle.com/code/janniskueck/ols-and-lasso-for-gender-wage-gap-inference/notebook	https://github.com/CausalAIBook/MetricsMLNotebooks/blob/main/PM1/inference.ipynb	Same as above, but links from Book
13	PM-1	https://www.kaggle.com/janniskueck/pm1-notebook-inference	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/PM1/inference.ipynb	An inferential Problem: The Gender Wage Gap
14	PM-1	https://www.kaggle.com/janniskueck/ols-and-lasso-for-wage-prediction	https://github.com/CausalAIBook/MetricsMLNotebooks/blob/main/	Same as above, but links from Book
15	PM-2A	https://www.kaggle.com/janniskueck/ml-for-wage-prediction	PM1/PM1_prediction.ipynb	ML for prediction of Wages
16	PM-2A	https://www.kaggle.com/victorchernozhukov/r-notebook-linear-penalized-regs	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/PM1/inference.ipynb	implementation of different penalized regression methods
17	PM-2B	https://www.kaggle.com/victorchernozhukov/heterogenous-wage-effects	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/PM2/heterogenous-wage-effects.ipynb	Heterogeneous Treatment Effects: CPS 2012
18	PM-2B	https://www.kaggle.com/janniskueck/double-lasso-for-the-convergence-hypothesis	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/PM2/pm2-notebook-jannis.ipynb	CPS 2015
19	PM-2B	https://www.kaggle.com/victorchernozhukov/r-notebook-experiment-on-orthogonal-learning	https://colab.research.google.com/github/CausalAIBook/MetricsMLNotebooks/blob/main/PM2/r-notebook-experiment-on-orthogonal-learning.ipynb	simulation experiment comparing orthogonal (partialling-out) with nonorthogonal learning
20	PM-3	https://www.kaggle.com/janniskueck/pm3-notebook-newdata		Wage Prediction with Machine Learning (cf PM 2A)
21	PM-3	https://www.kaggle.com/janniskueck/pm3-notebook-newdata-nn		Wage Prediction with Neural Nets
22	PM-3	https://www.kaggle.com/janniskueck/automl-for-wage-prediction		Wage Prediction with AutoML
23	PM-4	https://www.kaggle.com/janniskueck/dml-inference-for-gun-ownership		Inferential Problem with Machine Learning: Gun Ownership
24	PM-4	https://www.kaggle.com/janniskueck/dml-inference-using-nn-for-gun-ownership		Inferential Problem with Neural Nets: Gun Ownership
25	PM-4	https://www.kaggle.com/victorchernozhukov/identification-analysis-of-401-k-example-w-dags		identification of the causal effect of 401(K) eligibility on net financial wealth
26	PM-4	https://www.kaggle.com/janniskueck/dml-for-ate-and-late-of-401-k-on-wealth		ATE for 401(k) offer, LATE for 401(k) participation
27	PM-4	https://www.kaggle.com/victorchernozhukov/debiased-ml-for-partially-linear-iv-model-in-r		Double Machine Learning (IV and Linear): Impact of Institutions on Wealth
28	PM-5		https://colab.research.google.com/drive/1FI6pvlgZI2jUmFJb-C0OB00R0rgfugyH?usp=sharing	introduction to variational auto-encoders
29	PM-5		https://colab.research.google.com/drive/1j05fyqSHXw57aNQn6n6aSjVFiygihUWH?usp=sharing	introduction to text embeddings via BERT
30	T-1	https://www.kaggle.com/victorchernozhukov/sensitivity-analysis-with-sensmakr-and-debiased-ml		sensitivity analysis based on DML
31	T-1	https://www.kaggle.com/victorchernozhukov/debiased-ml-for-partially-linear-iv-model-in-r		DML IV analysis of the Acemoglu-Johnson-Robinson example
32	T-1	https://www.kaggle.com/janniskueck/dml-for-ate-and-late-of-401-k-on-wealth		Cf. PM-4
33	T-2	https://www.kaggle.com/victorchernozhukov/r-weak-iv-experiments		simulation experiment illustrating the weak instruments
34	T-2	https://www.kaggle.com/victorchernozhukov/debiased-ml-for-partially-linear-iv-model-in-r		DML analysis of the impact of institutions on a country’s wealth following AJR
35	T-3	https://www.kaggle.com/victorchernozhukov/dml-for-conditional-average-treatment-effect?scriptVersionId=63137207		ATE of 401(K) conditional on income
36	T-4			Differences-in-Differences
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