2014
DOI: 10.1007/s10618-014-0383-9
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Ensemble methods for uplift modeling

Abstract: Uplift modeling is a branch of machine learning which aims at predicting the causal effect of an action such as a marketing campaign or a medical treatment on a given individual by taking into account responses in a treatment group, containing individuals subject to the action, and a control group serving as a background. The resulting model can then be used to select individuals for whom the action will be most profitable. This paper analyzes the use of ensemble methods: bagging and random forests in uplift m… Show more

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Cited by 72 publications
(56 citation statements)
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References 26 publications
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“…Given that none of the tree-based techniques has been employed for modeling revenue uplift in e-commerce, the evaluation also broadens the scope of empirical results for causal machine learning methods. Causal Forest Cai et al (2011) Two-Step Estimation Procedure Chickering and Heckerman (2000) Uplift Tree with Post-Processing Procedure Diemert et al (2018) x - Guelman et al (2015a) Causal Conditional Inference Tree/Forest Guelman et al (2015b) Uplift Random Forests Gutierrez and Gérardy (2017) -x Hahn et al (2019) Causal Bayesian Regression Trees Hansen and Bowers (2008) x - Hansotia and Rukstales (2002a) Incremental Response Tree Hansotia and Rukstales (2002b) Uplift Tree with the ∆∆ splitting criterion Causal BART Imai and Ratkovic (2013) Uplift Support Vector Machine Jaroszewicz and Rzepakowski (2014) Uplift k-Nearest Neighbors Kane et al (2014) x - Kuusisto et al (2014) Uplift Support Vector Machine Künzel et al (2019) X-Learner Lai et al (2006) x - Lechner (2019) Modified Causal Forests Lo (2002) x - Lo and Pachamanova (2015) Multiple Treatments Logistic Regression Nassif et al (2013) x Oprescu et al (2018) Orthogonal Causal Random Forest Powers et al (2018) Causal boosting Radcliffe and Surry (1999) Uplift Trees Radcliffe and Surry (2011) -x Rzepakowski and Jaroszewicz (2012a) Multiple Treatments Uplift Trees Rzepakowski and Jaroszewicz (2012b) Information Theory-Based Uplift Trees Rudaś and Jaroszewicz (2018) x - Shaar et al (2016) Pessimistic Uplift Shalit et al (2017) Causal Artificial Neural Network Sołtys et al (2015) Uplift Ensemble Methods Su et al (2012) Uplift k-Nearest Neighbors Taddy et al (2016) Causal Bayesian Forests Tian et al (2014) x - Yamane et al (2018) Separate Label Uplift Modeling This study…”
Section: Background and Related Workmentioning
confidence: 99%
“…Given that none of the tree-based techniques has been employed for modeling revenue uplift in e-commerce, the evaluation also broadens the scope of empirical results for causal machine learning methods. Causal Forest Cai et al (2011) Two-Step Estimation Procedure Chickering and Heckerman (2000) Uplift Tree with Post-Processing Procedure Diemert et al (2018) x - Guelman et al (2015a) Causal Conditional Inference Tree/Forest Guelman et al (2015b) Uplift Random Forests Gutierrez and Gérardy (2017) -x Hahn et al (2019) Causal Bayesian Regression Trees Hansen and Bowers (2008) x - Hansotia and Rukstales (2002a) Incremental Response Tree Hansotia and Rukstales (2002b) Uplift Tree with the ∆∆ splitting criterion Causal BART Imai and Ratkovic (2013) Uplift Support Vector Machine Jaroszewicz and Rzepakowski (2014) Uplift k-Nearest Neighbors Kane et al (2014) x - Kuusisto et al (2014) Uplift Support Vector Machine Künzel et al (2019) X-Learner Lai et al (2006) x - Lechner (2019) Modified Causal Forests Lo (2002) x - Lo and Pachamanova (2015) Multiple Treatments Logistic Regression Nassif et al (2013) x Oprescu et al (2018) Orthogonal Causal Random Forest Powers et al (2018) Causal boosting Radcliffe and Surry (1999) Uplift Trees Radcliffe and Surry (2011) -x Rzepakowski and Jaroszewicz (2012a) Multiple Treatments Uplift Trees Rzepakowski and Jaroszewicz (2012b) Information Theory-Based Uplift Trees Rudaś and Jaroszewicz (2018) x - Shaar et al (2016) Pessimistic Uplift Shalit et al (2017) Causal Artificial Neural Network Sołtys et al (2015) Uplift Ensemble Methods Su et al (2012) Uplift k-Nearest Neighbors Taddy et al (2016) Causal Bayesian Forests Tian et al (2014) x - Yamane et al (2018) Separate Label Uplift Modeling This study…”
Section: Background and Related Workmentioning
confidence: 99%
“…Several approaches are based on decision trees Jaroszewicz 2010, 2012; Radcliffe and Surry 2011) trained using modified splitting criteria which aim to maximize differences in responses between groups, or some related information theoretical measure. Several works investigate combining such trees into ensembles (Guelman et al 2012;Sołtys et al 2014). Work on linear uplift models includes approaches based on class variable transformation (Lai 2006;Jaśkowski and Jaroszewicz 2012;Kane et al 2014;Pechyony et al 2013) used with logistic regression and approaches based on Support Vector Machines (Kuusisto et al 2014;Jaroszewicz 2013, Oct 2017).…”
Section: Literature Overviewmentioning
confidence: 99%
“…-The Bladder data set (Therneau 2015) contains information regarding recurrence of bladder cancer for three treatment groups: 1) pyridoxine, 2) thiotepa, and 3) placebo. As in Sołtys et al (2015), patients who had remaining cancer, or at least one recurrence, are classified as negative cases. -The colon data set (Therneau 2015) includes data of chemotherapy trials against colon cancer.…”
Section: Data Setsmentioning
confidence: 99%