Uplift models directly model the impact of a treatment or intervention by predicting the difference in outcomes between treated and untreated groups, rather than just the overall outcome probabilities. They are trained on data that includes treatment assignment and outcomes. Validation is done using uplift curves rather than standard classification metrics, by plotting a cumulative sum of positive responses minus controls. Basic approaches include training separate models on treated and control groups then subtracting predictions, or modifying decision tree splitting to favor treatments with higher uplift. Ensemble methods like random forests can also be adapted for uplift modeling.

1. Uplift models
Paulius Šarka

2. Image from http://www.supplychaincowboy.com/epic-warehouse-warning-signs/

3. tl;dr
Uplift model: directly models the impact of a treatment

4. Contents
1. Context for uplift models
2. What is an uplift model
3. Building an uplift model

5. Image from https://www.facebook.com/thechurn/
1.

6. The X and the y
X y
Apply:
Train:
X p
m
m

7. The treatment
p
Image from https://mashable.com/2011/10/03/anti-spam-guide/

8. The effect
Image from http://scientificmarketer.com/2007/02/fundamental-campaign-segmentation.html/

9. Classical targets p

10. Uplift targets

11. Image from http://unitedforkliftllc.com/parts.html
2.

12. Uplift model
X y m
Apply:
Train:
X δ
t
m

13. Uplift model
X y
Apply:
Train:
X δ
tt
δ
= 1 if targeted else 0
= P(churn | t = 1)
- P(churn | t = 0)
m
m

14. The effect (again)

15. Minimal Viable Pipeline
Xy
δ
6. Apply uplift model
1. Some action
8. More results
y
Pls stay!
t
4. Train uplift model
Pls stay!
7. More action
2. Collect results
9. Profit!
3. Create features 5. Validate model
X
m
m

16. Validation
● True value of δ is unobservable, hence usual metrics do not have
equivalents:
✘ MSE
✘ Accuracy & Precision
✘ Log loss
✘ ROC curve
● But some have!
✓ Lift curve

17. Lift curve of a classifier
1. Compute test set predictions
2. Sort users by predicted score
3. Plot a cumulative sum of
positive responses
Data needed: p, y

18. Uplift curve of an uplift model
1. Compute test set predictions
2. Sort users by predicted score
3. Plot a cumulative sum of
positive target responses minus
positive control responses
Data needed: δ, y, t

19. One more time!
Uplift model Binary classifier
P(y=1|X, t=1) - P(y=1|X, t=0) P(y=1|X)
ulf.fit(X, y, t) clf.fit(X, y)
ulf.predict_uplift(X) clf.predict_proba(X)
uplift_curve(δ, y, t) lift_curve(p, y)

20. Image from https://www.pinterest.co.uk/pin/144537469272890024/
3.

21. Poor man’s uplift model
Split the dataset in two:
X_t = X[t=1], X_c = X[t=0]
y_t = y[t=1], y_c = y[t=0]
Train two models:
clf_t.fit(X_t, y_t), clf_c.fit(X_c, y_c)
Subtract:
δ = clf_t.predict_proba(X) - clf_c.predict_proba(X)

22. Better: hack a decision tree
● Find a way to feed t
● Modify the splitting rule
Image from https://www.linkedin.com/pulse/how-does-id3-algorithm-works-decision-trees-sagarnil-das/

23. Better: hack a decision tree
➕ Positive uplift to the left
➖ Negative uplift to the right*
*conditions apply
» More ideas in the papers!
Image from https://www.linkedin.com/pulse/how-does-id3-algorithm-works-decision-trees-sagarnil-das/

24. Also
✓ Bagging
✓ Random forest
✓ Feature importances*
✓ Per-user feature importances*
* probably

25. Resources
1999, N.Radcliffe, P.Surry, Differential Response Analysis: Modeling True Responses by Isolating the Effect of a Single Action
2002, B.Hansotia, B.Rukstales, Incremental Value Modeling
2007, N.Radcliffe, Using Control Groups to Target on Predicted Lift: Building and Assessing Uplift Models
2010, P.Rzepakowski, S.Jaroszewicz, Decision trees for uplift modeling
2011, N.Radcliffe, P.Surry, Real-World Uplift Modelling with Significance-Based Uplift Trees
2012, P.Rzepakowski, S.Jaroszewicz, Decision trees for uplift modeling with single and multiple treatments
2015, L.Guelman, M.Guillen, M.Perez-Marin, Uplift Random Forests
2015, M.Soltys, S.Jaroszewicz, P.Rzepakowski, Ensemble methods for uplift modeling
2017, W.Verbeke, C.Bravo, B.Baesens, Profit drive business analytics: A practitioner's guide to transforming big data into added value. (Chapter 4)
Wiki page: https://en.wikipedia.org/wiki/Uplift_modelling
R package: http://cran.r-project.org/web/packages/uplift/index.html
some python code: https://github.com/psarka/uplift