Causality

Notes, exercises and other materials related to causal inference, causal discovery and causal ML.

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Name		Name	Last commit message	Last commit date
data		data
docs/superpowers		docs/superpowers
img		img
papers/uplift		papers/uplift
utils		utils
.Rhistory		.Rhistory
.gitignore		.gitignore
00 - Causal discovery II Notes 1.ipynb		00 - Causal discovery II Notes 1.ipynb
01 - Graphs and independence.ipynb		01 - Graphs and independence.ipynb
02 - DoWhy + EconML - Process example.ipynb		02 - DoWhy + EconML - Process example.ipynb
03 - SCMs.ipynb		03 - SCMs.ipynb
04 - Causal discovery with gCastle.ipynb		04 - Causal discovery with gCastle.ipynb
05 - Causal discovery from scratch - ANM.ipynb		05 - Causal discovery from scratch - ANM.ipynb
06 - Causal discovery from scratch - IGCI.ipynb		06 - Causal discovery from scratch - IGCI.ipynb
07 - Counterfactuals example.ipynb		07 - Counterfactuals example.ipynb
09 - DAG - identify no. of paths of len k.ipynb		09 - DAG - identify no. of paths of len k.ipynb
10 - Check if G is a DAG.ipynb		10 - Check if G is a DAG.ipynb
11 - DECI notes.ipynb		11 - DECI notes.ipynb
15 - Causation vs correlation.ipynb		15 - Causation vs correlation.ipynb
16 - Causal discovery - PC variants.ipynb		16 - Causal discovery - PC variants.ipynb
18 - Encoding expert knowledge.ipynb		18 - Encoding expert knowledge.ipynb
19 - Detecting outliers with GCMs.ipynb		19 - Detecting outliers with GCMs.ipynb
21 - Synthetic data with gCastle.ipynb		21 - Synthetic data with gCastle.ipynb
22 - Causal discovery - PC with expert knowledge.ipynb		22 - Causal discovery - PC with expert knowledge.ipynb
24 - Estimating Causal Impact.ipynb		24 - Estimating Causal Impact.ipynb
26 - Challenges with PSM.ipynb		26 - Challenges with PSM.ipynb
26b - Challenges with PSM 2.ipynb		26b - Challenges with PSM 2.ipynb
27 - Control Scheme vs Estimator Variance.ipynb		27 - Control Scheme vs Estimator Variance.ipynb
28 - DoWhy with binary outcomes.ipynb		28 - DoWhy with binary outcomes.ipynb
29 - Intro to pyAgrum - (Causal) Bayesian Networks.ipynb		29 - Intro to pyAgrum - (Causal) Bayesian Networks.ipynb
30 - DoWhy GCM API with Categorical & Mixed Data.ipynb		30 - DoWhy GCM API with Categorical & Mixed Data.ipynb
32 - DML Misspecification.ipynb		32 - DML Misspecification.ipynb
34 - Decision-Making Errors Pt 1.ipynb		34 - Decision-Making Errors Pt 1.ipynb
35 - Low Power vs Effect Exaggeration (M-Type Error).ipynb		35 - Low Power vs Effect Exaggeration (M-Type Error).ipynb
36 - EconML DML Double Robustness.ipynb		36 - EconML DML Double Robustness.ipynb
37 - EconML DML Double_Robustness - NonParam.ipynb		37 - EconML DML Double_Robustness - NonParam.ipynb
38 - Optimal A-B Test Split.ipynb		38 - Optimal A-B Test Split.ipynb
40 - Synth Control Inference.ipynb		40 - Synth Control Inference.ipynb
40 - Synth Control Inference.py		40 - Synth Control Inference.py
41 - Clustered Randomization.ipynb		41 - Clustered Randomization.ipynb
42 - Proximal Inference Basics - 2SLS.ipynb		42 - Proximal Inference Basics - 2SLS.ipynb
CLAUDE.md		CLAUDE.md
LICENSE		LICENSE
README.md		README.md
add_dataviz.ipynb		add_dataviz.ipynb
causal-ts-env.yml		causal-ts-env.yml
causal_book_py38_dw09.yml		causal_book_py38_dw09.yml
causal_model.png		causal_model.png
causalml-py38.yml		causalml-py38.yml
econml-dowhy-py38.yml		econml-dowhy-py38.yml
requirements-synthcontrol.txt		requirements-synthcontrol.txt

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