causalml
huigang/doc_update
About Causal ML
Methodology
Installation
Examples
Interpretable Causal ML
Validation
causalml package
References
Changelog
causalml
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Welcome to Causal ML’s documentation
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Welcome to Causal ML’s documentation
¶
Contents:
About Causal ML
Methodology
Meta-Learner Algorithms
Tree-Based Algorithms
Value optimization methods
Selected traditional methods
Targeted maximum likelihood estimation (TMLE) for ATE
Installation
Install using
conda
Install using
pip
Install from source
Examples
Propensity Score
Average Treatment Effect (ATE) Estimation
More algorithms
Interpretation
Validation
Synthetic Data Generation Process
Sensitivity Analysis
Feature Selection
Interpretable Causal ML
Meta-Learner Feature Importances
Uplift Tree Visualization
Uplift Tree Feature Importances
Validation
Validation with Multiple Estimates
Validation with Synthetic Data Sets
Validation with Uplift Curve (AUUC)
Validation with Sensitivity Analysis
causalml package
Submodules
causalml.inference.tree module
causalml.inference.meta module
causalml.optimize module
causalml.dataset module
causalml.match module
causalml.propensity module
causalml.metrics module
Module contents
References
Open Source Software Projects
Papers
Changelog
0.11.0 (2021-07-28)
0.10.0 (2021-02-18)
0.9.0 (2020-10-23)
0.8.0 (2020-07-17)
0.7.1 (2020-05-07)
0.7.0 (2020-02-28)
0.6.0 (2019-12-31)
0.5.0 (2019-11-26)
0.4.0 (2019-10-21)
0.3.0 (2019-09-17)
0.2.0 (2019-08-12)
0.1.0 (unreleased)
Indices and tables
¶
Index
Module Index
Search Page
Read the Docs
v: huigang/doc_update
Versions
latest
stable
huigang-doc_update
Downloads
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