causeinfer 0.0.3

Causal inference/uplift in Python

• Causal inference/uplift in Python

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Application • Included Datasets • Contribute • References • License

Installation

pip install causeinfer

Application

Causal inference algorithms:

1. The Two Model Approach

• Separate models for treatment and control groups are trained and combined to derive average treatment effects.

2. Interaction Term Approach - Lo 2002

• An interaction term between treatment and covariates is added to the data to allow for a basic single model application.

3. Response Transformation Approach - Lai 2006; Kane, Lo and Zheng 2014

• Units are categorized to allow for the derivation of treatment effected covariates through classification.

4. Generalized Random Forest - Athey, Tibshirani, and Wager 2019

• An application of an honest causalaity based splitting random forest.

Evaluation metrics:

1. Qini and AUUC Scores

• Comparisons across stratefied, ordered treatment response groups are used to derive model efficiency

2. GRF Confidence Intervals

• Confidence intervals are created using GRF's standard deviation across trials

Included Datasets

• Hillstrom Email Marketing
• Mayo Clinic PBC
• IFMR Microfinance

Contribute

Contributions are more than welcome!

• Examples: share more applications
• Issues: add, or see what's to be done

Similar Packages

The following are similar packages/modules to causeinfer:

Python:

• https://github.com/uber/causalml
• https://github.com/Minyus/causallift
• https://github.com/jszymon/uplift_sklearn
• https://github.com/duketemon/pyuplift
• https://github.com/microsoft/EconML
• https://github.com/wayfair/pylift/

Other Languages:

• https://github.com/grf-labs/grf (R/C++)
• https://github.com/imbs-hl/ranger (R/C++)
• https://github.com/soerenkuenzel/causalToolbox/blob/a06d81d74f4d575a8b34dc6b718db2778cfa0be9/R/XRF.R (R/C++)
• https://github.com/soerenkuenzel/forestry (R/C++)
• https://github.com/cran/uplift/tree/master/R (R)

References

Full list of theoretical references

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