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Explore an article below and implement dummy and interactions approach or DIA - multitreatment approach for uplift modeling.
This is an extension of binary sklift.models.SoloModel to multitreatment case.
Additional context
This method extends the input space by adding treatment indicators encoded as dummies 𝐷={0,…,𝑘} and interaction terms. The latter capture the interplay between the dummies and the pretreatment characteristics. Uplift is then modeled by means of any machine learning algorithm that receives as input the pretreatment characteristics X, the dummy variables D, and the interaction terms 𝐷×𝑋, so that 𝑃(𝑌=1|𝑋,𝑑𝑜(𝑇))=𝑓(𝑋,𝐷,𝐷×𝑋).
💡 Feature request
Explore an article below and implement
dummy and interactions approach
orDIA
- multitreatment approach for uplift modeling.This is an extension of binary
sklift.models.SoloModel
to multitreatment case.Additional context
This method extends the input space by adding treatment indicators encoded as dummies 𝐷={0,…,𝑘} and interaction terms. The latter capture the interplay between the dummies and the pretreatment characteristics. Uplift is then modeled by means of any machine learning algorithm that receives as input the pretreatment characteristics X, the dummy variables D, and the interaction terms 𝐷×𝑋, so that 𝑃(𝑌=1|𝑋,𝑑𝑜(𝑇))=𝑓(𝑋,𝐷,𝐷×𝑋).
Please follow the article with the
DIA
approachChen X, Owen Z, Pixton C, Simchi-Levi D (2015) A statistical learning approach to personalization in revenue management. SSRN Electronic Journal
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