Warning
This approach is only suitable for classification problem
Simple yet powerful and mathematically proven uplift modeling method, presented in 2012. The main idea is to predict a slightly changed target Zi:
Zi = Yi ⋅ Wi + (1 − Yi) ⋅ (1 − Wi),
- Zi - a new target for the i customer;
- Yi - a previous target for the i customer;
- Wi - treatment flag assigned to the i customer.
In other words, the new target equals 1 if a response in the treatment group is as good as a response in the control group and equals 0 otherwise:
Let's go deeper and estimate the conditional probability of the target variable:
We assume that W is independent of X = x by design. Thus we have: P(W|X = x) = P(W) and
Also, we assume that
Thus, by doubling the estimate of the new target Z and subtracting one we will get an estimation of the uplift:
uplift = 2 ⋅ P(Z = 1) − 1
This approach is based on the assumption:
Hint
In sklift this approach corresponds to the .ClassTransformation
class.
1️⃣ Maciej Jaskowski and Szymon Jaroszewicz. Uplift modeling for clinical trial data. ICML Workshop on Clinical Data Analysis, 2012.
- The overview of the basic approaches to the Uplift Modeling problem
In English 🇬🇧 | nbviewer | github | |
In Russian 🇷🇺 | nbviewer | github |
- The 2nd place solution of X5 RetailHero uplift contest by Kirill Liksakov
In English 🇬🇧 | nbviewer | github |