InterpretableMachineLearning2020

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Problem

This is a binary classification problem. On the basis of historical data, models (of varying degrees of complexity) should be developed to predict the purchase uplift of marketing offer (uplift modelling). The best models should be explained using XAI tools at the instance level and at the data set level.

Data

Datasets: (1) 'train' and (2) 'valid' from R package named 'Information'

Example solution

Two interesting solutions for this dataset are described under the links https://www.profit-analytics.com/examples/ch-4-uplift-examples/uplift-modeling-example-two-model-approach/ https://humboldt-wi.github.io/blog/research/theses/uplift_modeling_blogpost/

Additional learning materials and implementations:

R grf package Generalized Random Forests https://github.com/grf-labs/grf R uplift package: https://cran.r-project.org/web/packages/uplift/index.html R tools4uplift package: https://cran.r-project.org/web/packages/tools4uplift/index.html R BART package, vignettes: https://rdrr.io/cran/BART/ Python: Microsoft ALICE https://github.com/microsoft/EconML Python: Uber's CausalML https://github.com/uber/causalml

Hi Data enthusiasts! I highly encourage you to investigate methods of uplift modelling, which are trully game changers in the world of business analytics. I will be happy to tell you more about these methods and applications. Since, it's still a very novel method, your book chapter about it might be very popular. Reach to me, if you have any questions and happy book writing! :)