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data
data
 
 
docs/superpowers
docs/superpowers
 
 
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papers/uplift
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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
 
 
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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Causality

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

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

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