[ot] Automated Influence with Causality Modeling

k gmkarl at gmail.com
Tue Dec 28 06:24:26 PST 2021 

https://en.m.wikipedia.org/wiki/Uplift_modelling

Implementations include 5 python libraries.  I found UpliftML in a 2021
review of recommended python libraries.
Uplift modelling

*Uplift modelling*, also known as *incremental modelling*, *true lift
modelling*, or *net modelling* is a predictive modelling
<https://en.m.wikipedia.org/wiki/Predictive_modelling> technique that
directly models the incremental impact of a treatment (such as a direct
marketing action) on an individual's behaviour.

Uplift modelling has applications in customer relationship management
<https://en.m.wikipedia.org/wiki/Customer_relationship_management> for
up-sell, cross-sell and retention modelling. It has also been applied to
political election and personalised medicine
<https://en.m.wikipedia.org/wiki/Personalised_medicine>. Unlike the related
Differential Prediction concept in psychology, Uplift Modelling assumes an
active agent.
Contents

   - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Introduction>
   - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Measuring_uplift>
   -
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#Traditional_response_modelling>
   - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Return_on_investment>
   -
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#Removal_of_negative_effects>
   -
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#Application_to_A/B_and_multivariate_testing>
   -
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#History_of_uplift_modelling>
   - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Implementations>
      - <https://en.m.wikipedia.org/wiki/Uplift_modelling#In_Python>
      - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Other_languages>
   - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Datasets>
   - <https://en.m.wikipedia.org/wiki/Uplift_modelling#Notes_and_references>
   - <https://en.m.wikipedia.org/wiki/Uplift_modelling#See_also>
   - <https://en.m.wikipedia.org/wiki/Uplift_modelling#External_links>

IntroductionEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=1>

Uplift modelling uses a randomised scientific control
<https://en.m.wikipedia.org/wiki/Control_group> to not only measure the
effectiveness of an action but also to build a predictive model that
predicts the incremental response to the action. The response could be a
binary variable (for example, a website visit)[1]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-:0-1> or a
continuous variable (for example, customer revenue).[2]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-2> Uplift
modelling is a data mining
<https://en.m.wikipedia.org/wiki/Data_mining> technique
that has been applied predominantly in the financial services,
telecommunications and retail direct marketing industries to up-sell
<https://en.m.wikipedia.org/wiki/Up-selling>, cross-sell
<https://en.m.wikipedia.org/wiki/Cross-selling>, churn
<https://en.m.wikipedia.org/wiki/Churn_rate> and retention
<https://en.m.wikipedia.org/wiki/Customer_retention> activities.
Measuring upliftEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=2>

The uplift of a marketing campaign is usually defined as the difference in
response rate between a *treated* group and a randomized *control* group.
This allows a marketing team to isolate the effect of a marketing action
and measure the effectiveness or otherwise of that individual marketing
action. Honest marketing teams will only take credit for the incremental
effect of their campaign.

However, many marketers define lift (rather than uplift) as the difference
in response rate between treatment and control, so uplift modeling can be
defined as improving (upping) lift through predictive modeling.

The table below shows the details of a campaign showing the number of
responses and calculated response rate for a hypothetical marketing
campaign. This campaign would be defined as having a response rate uplift
of 5%. It has created 50,000 incremental responses (100,000 - 50,000).
GroupNumber of CustomersResponsesResponse Rate
Treated 1,000,000 100,000 10%
Control 1,000,000 50,000 5%
Traditional response modellingEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=3>

Traditional response modelling typically takes a group of *treated* customers
and attempts to build a predictive model that separates the likely
responders from the non-responders through the use of one of a number
of predictive
modelling <https://en.m.wikipedia.org/wiki/Predictive_modelling> techniques.
Typically this would use decision trees
<https://en.m.wikipedia.org/wiki/Decision_tree_learning> or regression
analysis <https://en.m.wikipedia.org/wiki/Regression_analysis>.

This model would only use the treated customers to build the model.

In contrast uplift modeling uses both the treated and control customers to
build a predictive model that focuses on the incremental response. To
understand this type of model it is proposed that there is a fundamental
segmentation that separates customers into the following groups (their
names were suggested by N. Radcliffe and explained in [3]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-3>)

   - *The Persuadables* : customers who only respond to the marketing
   action because they were targeted
   - *The Sure Things*  : customers who would have responded whether they
   were targeted or not
   - *The Lost Causes*  : customers who will not respond irrespective of
   whether or not they are targeted
   - *The Do Not Disturbs or Sleeping Dogs* : customers who are less likely
   to respond because they were targeted

The only segment that provides true incremental responses is the
*Persuadables*.

Uplift modelling provides a scoring technique that can separate customers
into the groups described above.

Traditional response modelling often targets the *Sure Things* being unable
to distinguish them from the *Persuadables*.
Return on investmentEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=4>

Because uplift modelling focuses on incremental responses only, it provides
very strong return on investment cases when applied to traditional demand
generation and retention activities. For example, by only targeting the
persuadable customers in an outbound marketing
<https://en.m.wikipedia.org/wiki/Marketing_communications> campaign, the
contact costs and hence the return per unit spend can be dramatically
improved.
Removal of negative effectsEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=5>

One of the most effective uses of uplift modelling is in the removal of
negative effects from retention campaigns. Both in the telecommunications
and financial services industries often retention campaigns can trigger
customers to cancel a contract or policy. Uplift modelling allows these
customers, the Do Not Disturbs, to be removed from the campaign.
Application to A/B and multivariate testingEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=6>

It is rarely the case that there is a single treatment and control group.
Often the "treatment" can be a variety of simple variations of a message or
a multi-stage contact strategy that is classed as a single treatment. In
the case of A/B <https://en.m.wikipedia.org/wiki/A/B_testing> or multivariate
testing <https://en.m.wikipedia.org/wiki/Multivariate_testing_in_marketing>,
uplift modelling can help in understanding whether the variations in tests
provide any significant uplift compared to other targeting criteria such as
behavioural or demographic indicators.
History of uplift modellingEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=7>

The first appearance of *true response modelling* appears to be in the work
of Radcliffe and Surry.[4]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-4>

Victor Lo also published on this topic in *The True Lift Model* (2002),[5]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-5> and later
Radcliffe again with *Using Control Groups to Target on Predicted Lift:
Building and Assessing Uplift Models* (2007).[6]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-6>

Radcliffe also provides a very useful frequently asked questions (FAQ)
section on his web site, Scientific Marketer.[7]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-7> Lo (2008)
provides a more general framework, from program design to predictive
modeling to optimization, along with future research areas.[8]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-8>

Independently uplift modelling has been studied by Piotr Rzepakowski.
Together with Szymon Jaroszewicz he adapted information theory
<https://en.m.wikipedia.org/wiki/Information_theory> to build multi class
uplift decision trees <https://en.m.wikipedia.org/wiki/Decision_trees> and
published the paper in 2010.[9]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-uplift-trees-9> And
later in 2011 they extended the algorithm to multiple treatment case.[10]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-multiple-treatment-uplift-trees-10>

Similar approaches have been explored in personalised medicine
<https://en.m.wikipedia.org/wiki/Personalised_medicine>.[11]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-11>[12]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-SAYL-12> Szymon
Jaroszewicz and Piotr Rzepakowski (2014) designed uplift methodology
for survival
analysis <https://en.m.wikipedia.org/wiki/Survival_analysis> and applied it
to randomized controlled trial analysis.[13]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-uplift-survival-analysis-13>
Yong
(2015) combined a mathematical optimization algorithm via dynamic
programming with machine learning methods to optimally stratify patients.
[14] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-14>

Uplift modelling is a special case of the older psychology concept of
Differential Prediction.[15]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-RDP-15> In
contrast to differential prediction, uplift modelling assumes an active
agent, and uses the uplift measure as an optimization metric.

Uplift modeling has been recently extended and incorporated into
diverse machine
learning <https://en.m.wikipedia.org/wiki/Machine_learning> algorithms,
like Inductive Logic Programming
<https://en.m.wikipedia.org/wiki/Inductive_Logic_Programming>,[15]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-RDP-15> Bayesian
Network <https://en.m.wikipedia.org/wiki/Bayesian_Network>,[16]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-16> Statistical
relational learning
<https://en.m.wikipedia.org/wiki/Statistical_relational_learning>,[12]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-SAYL-12> Support
Vector Machines <https://en.m.wikipedia.org/wiki/Support_Vector_Machines>,
[17] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-17>[18]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-18> Survival
Analysis <https://en.m.wikipedia.org/wiki/Survival_Analysis>[13]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-uplift-survival-analysis-13>
 and Ensemble learning <https://en.m.wikipedia.org/wiki/Ensemble_learning>.
[19] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-19>

Even though uplift modeling is widely applied in marketing practice (along
with political elections), it has rarely appeared in marketing literature.
Kane, Lo and Zheng (2014) published a thorough analysis of three data sets
using multiple methods in a marketing journal and provided evidence that a
newer approach (known as the Four Quadrant Method) worked quite well in
practice.[20]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-20> Lo and
Pachamanova (2015) extended uplift modeling to prescriptive analytics for
multiple treatment situations and proposed algorithms to solve large
deterministic optimization problems and complex stochastic optimization
problems where estimates are not exact.[21]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-21>

Recent research analyses the performance of various state-of-the-art uplift
models in benchmark studies using large data amounts.[22]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-22>[1]
<https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-:0-1>

A detailed description of uplift modeling, its history, the way uplift
models are built, differences to classical model building as well as
uplift-specific evaluation techniques, a comparison of various software
solutions and an explanation of different economical scenarios can be found
here.[23] <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_note-23>
ImplementationsEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=8>In
PythonEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=9>

   - CausalML <https://github.com/uber/causalml>
   - EconML <https://github.com/microsoft/EconML>
   - UpliftML <https://github.com/bookingcom/upliftml>
   - PyLift <https://github.com/wayfair/pylift>
   - scikit-uplift <https://github.com/maks-sh/scikit-uplift>

Other languagesEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=10>

   - uplift package
   <https://cran.r-project.org/web/packages/uplift/index.html> for R
   <https://en.m.wikipedia.org/wiki/R_%28programming_language%29>
   - JMP <https://en.m.wikipedia.org/wiki/JMP_%28statistical_software%29> by
   SAS
   - Portrait Uplift by Pitney Bowes
   <https://en.m.wikipedia.org/wiki/Pitney_Bowes>
   - Uplift node for KNIME <https://en.m.wikipedia.org/wiki/KNIME> by
   Dymatrix
   - Uplift Modelling in Miró
<http://www.stochasticsolutions.com/miro/> by Stochastic
   Solutions <http://www.stochasticsolutions.com/>

DatasetsEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=11>

   - Hillstrom Email Marketing dataset
   <https://blog.minethatdata.com/2008/05/best-answer-e-mail-analytics-challenge.html>
   - Criteo Uplift Prediction dataset
   <http://ailab.criteo.com/criteo-uplift-prediction-dataset/>
   - Lenta Uplift Modeling Dataset
   <https://www.uplift-modeling.com/en/latest/api/datasets/fetch_lenta.html#lenta-uplift-modeling-dataset>
   - X5 RetailHero Uplift Modeling Dataset
   <https://www.uplift-modeling.com/en/latest/api/datasets/fetch_x5.html#x5-retailhero-uplift-modeling-dataset>
   - MegaFon Uplift Competition Dataset
   <https://www.uplift-modeling.com/en/latest/api/datasets/fetch_megafon.html#megafon-uplift-competition-dataset>

Notes and referencesEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=12>

   1. ^ *a*
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-:0_1-0> *b*
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-:0_1-1>
Devriendt,
   Floris; Moldovan, Darie; Verbeke, Wouter (2018). "A literature survey and
   experimental evaluation of the state-of-the-art in uplift modeling: A
   stepping stone toward the development of prescriptive analytics". *Big
   Data*. *6* (1): 13–41. doi
   <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:
   10.1089/big.2017.0104 <https://doi.org/10.1089%2Fbig.2017.0104>.
   2. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-2>* Gubela,
   Robin M.; Lessmann, Stefan; Jaroszewicz, Szymon (2020). "Response
   transformation and profit decomposition for revenue uplift
modeling". *European
   Journal of Operational Research*. *283* (2): 647–661. doi
   <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:
   10.1016/j.ejor.2019.11.030 <https://doi.org/10.1016%2Fj.ejor.2019.11.030>
   .
   3. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-3>* N.
   Radcliffe (2007). *Identifying who can be saved and who will be driven
   away by retention activity*. Stochastic Solution Limited
   4. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-4>*
Radcliffe,
   N. J.; and Surry, P. D. (1999); *Differential response analysis:
   Modelling true response by isolating the effect of a single
action*, in *Proceedings
   of Credit Scoring and Credit Control VI*, Credit Research Centre,
   University of Edinburgh Management School
   5. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-5>* Lo,
   V. S. Y. (2002); *The True Lift Model*, ACM SIGKDD Explorations
   Newsletter, Vol. 4, No. 2, 78–86, available at
   http://www.sigkdd.org/sites/default/files/issues/4-2-2002-12/lo.pdf
   Archived
   <https://web.archive.org/web/20150120003523/http://www.sigkdd.org/sites/default/files/issues/4-2-2002-12/lo.pdf>
2015-01-20
   at the Wayback Machine <https://en.m.wikipedia.org/wiki/Wayback_Machine>
   6. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-6>*
Radcliffe,
   N. J. (2007); *Using Control Groups to Target on Predicted Lift:
   Building and Assessing Uplift Models*, Direct Marketing Analytics
   Journal, Direct Marketing Association
   7. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-7>* The
   Scientific Marketer FAQ on Uplift Modelling
   <http://scientificmarketer.com/2007/09/uplift-modelling-faq.html>
   8. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-8>* Lo,
   V. S.Y. (2008) “New Opportunities in Marketing Data Mining.” In
   Encyclopedia of Data Warehousing and Mining, 2nd edition, edited by Wang
   (2008), Idea Group Publishing.
   9. *^
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-uplift-trees_9-0>*
    Rzepakowski, Piotr; Jaroszewicz, Szymon (2010). *Decision trees for
   uplift modeling*. *In Proceedings of the 10th IEEE International
   Conference on Data Mining (ICDM'10)*. Sydney, Australia. pp. 441–450. doi
   <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:
   10.1109/ICDM.2010.62 <https://doi.org/10.1109%2FICDM.2010.62>. ISBN
   <https://en.m.wikipedia.org/wiki/ISBN_%28identifier%29> 978-1-4244-9131-5
   <https://en.m.wikipedia.org/wiki/Special:BookSources/978-1-4244-9131-5>.
   10. *^
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-multiple-treatment-uplift-trees_10-0>*
    Rzepakowski, Piotr; Jaroszewicz, Szymon (2011). "Decision trees for
   uplift modeling with single and multiple treatments"
   <https://doi.org/10.1007%2Fs10115-011-0434-0>. *Knowledge and
   Information Systems*. *32* (2): 303–327. doi
   <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:
   10.1007/s10115-011-0434-0 <https://doi.org/10.1007%2Fs10115-011-0434-0>.
   11. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-11>* Cai,
   T.; Tian, L.; Wong, P. H.; and Wei, L. J. (2009); *Analysis of
   Randomized Comparative Clinical Trial Data for Personalized Treatment
   Selections*, Harvard University Biostatistics Working Paper Series,
   Paper 97
   12. ^ *a*
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-SAYL_12-0> *b*
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-SAYL_12-1>
Nassif,
   Houssam; Kuusisto, Finn; Burnside, Elizabeth S; Page, David; Shavlik, Jude;
   Santos Costa, Vitor (2013). *Score As You Lift (SAYL): A Statistical
   Relational Learning Approach to Uplift Modeling*. *European Conference
   on Machine Learning (ECML'13)*. Lecture Notes in Computer Science. *8190*.
   Prague. pp. 595–611. doi
   <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:
   10.1007/978-3-642-40994-3_38
   <https://doi.org/10.1007%2F978-3-642-40994-3_38>. ISBN
   <https://en.m.wikipedia.org/wiki/ISBN_%28identifier%29> 978-3-642-38708-1
   <https://en.m.wikipedia.org/wiki/Special:BookSources/978-3-642-38708-1>.
   PMC <https://en.m.wikipedia.org/wiki/PMC_%28identifier%29> 4492311
   <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4492311>. PMID
   <https://en.m.wikipedia.org/wiki/PMID_%28identifier%29> 26158122
   <https://pubmed.ncbi.nlm.nih.gov/26158122>.
   13. ^ *a*
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-uplift-survival-analysis_13-0>
    *b*
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-uplift-survival-analysis_13-1>
    Jaroszewicz, Szymon; Rzepakowski, Piotr (2014). "Uplift modeling with
   survival data" <http://cci.drexel.edu/hi/hi-kdd2014/morning_4.pdf> (PDF)
   . *ACM SIGKDD Workshop on Health Informatics (HI KDD'14)*. New York, USA.
   14. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-14>* Yong,
   F.H. (2015), "Quantitative Methods for Stratified Medicine," PhD
   Dissertation, Department of Biostatistics, Harvard T.H. Chan School of
   Public Health,
   http://dash.harvard.edu/bitstream/handle/1/17463130/YONG-DISSERTATION-2015.pdf?sequence=1
    .
   15. ^ *a*
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-RDP_15-0> *b*
   <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-RDP_15-1> Nassif,
   Houssam; Santos Costa, Vitor; Burnside, Elizabeth S; Page, David
(2012). *Relational
   Differential Prediction*. *European Conference on Machine Learning
   (ECML'12)*. Lecture Notes in Computer Science. *7523*. Bristol, UK.
   pp. 617–632. doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:
   10.1007/978-3-642-33460-3_45
   <https://doi.org/10.1007%2F978-3-642-33460-3_45>. ISBN
   <https://en.m.wikipedia.org/wiki/ISBN_%28identifier%29> 978-3-642-33459-7
   <https://en.m.wikipedia.org/wiki/Special:BookSources/978-3-642-33459-7>.
   16. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-16>*
Nassif,
   Houssam; Wu, Yirong; Page, David; Burnside, Elizabeth (2012). "Logical
   Differential Prediction Bayes Net, Improving Breast Cancer Diagnosis for
   Older Women"
<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540455>. *American
   Medical Informatics Association Symposium (AMIA'12)*. *2012*: 1330–1339.
   PMC <https://en.m.wikipedia.org/wiki/PMC_%28identifier%29> 3540455
   <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3540455>. PMID
   <https://en.m.wikipedia.org/wiki/PMID_%28identifier%29> 23304412
   <https://pubmed.ncbi.nlm.nih.gov/23304412>.
   17. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-17>*
Kuusisto,
   Finn; Santos Costa, Vitor; Nassif, Houssam; Burnside, Elizabeth; Page,
   David; Shavlik, Jude (2014). *Support Vector Machines for Differential
   Prediction*. *European Conference on Machine Learning (ECML'14)*.
   Lecture Notes in Computer Science. *8725*. Nancy, France. pp. 50–65. doi
   <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:
   10.1007/978-3-662-44851-9_4
   <https://doi.org/10.1007%2F978-3-662-44851-9_4>. ISBN
   <https://en.m.wikipedia.org/wiki/ISBN_%28identifier%29> 978-3-662-44850-2
   <https://en.m.wikipedia.org/wiki/Special:BookSources/978-3-662-44850-2>.
   PMC <https://en.m.wikipedia.org/wiki/PMC_%28identifier%29> 4492338
   <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4492338>. PMID
   <https://en.m.wikipedia.org/wiki/PMID_%28identifier%29> 26158123
   <https://pubmed.ncbi.nlm.nih.gov/26158123>.
   18. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-18>*
Zaniewicz,
   Lukasz; Jaroszewicz, Szymon (2013). "Support Vector Machines for Uplift
   Modeling". *The First IEEE ICDM Workshop on Causal Discovery*. Dallas,
   Texas.
   19. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-19>*
Sołtys,
   Michał; Jaroszewicz, Szymon; Rzepakowski, Piotr (2015). "Ensemble
   methods for uplift modeling"
   <https://doi.org/10.1007%2Fs10618-014-0383-9>. *Data Mining and
   Knowledge Discovery*. *29* (6): 1531–1559. doi
   <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:
   10.1007/s10618-014-0383-9 <https://doi.org/10.1007%2Fs10618-014-0383-9>.
   20. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-20>* Kane,
   K.; Lo, V.S.Y.; Zheng, J. (2014). "Mining for the Truly Responsive
   Customers and Prospects Using True-Lift Modeling: Comparison of New and
   Existing Methods". *Journal of Marketing Analytics*. *2* (4): 218–238.
   doi <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:
   10.1057/jma.2014.18 <https://doi.org/10.1057%2Fjma.2014.18>.
   21. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-21>* Lo,
   V.S.Y.; Pachamanova, D. (2015). "From Predictive Uplift Modeling to
   Prescriptive Uplift Analytics: A Practical Approach to Treatment
   Optimization While Accounting for Estimation Risk". *Journal of
   Marketing Analytics*. *3* (2): 79–95. doi
   <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:10.1057/jma.2015.5
   <https://doi.org/10.1057%2Fjma.2015.5>.
   22. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-22>*
Gubela,
   Robin M.; Bequé, Artem; Lessmann, Stefan; Gebert, Fabian (2019).
   "Conversion Uplift in E-Commerce: A Systematic Benchmark of Modeling
   Strategies". *International Journal of Information Technology & Decision
   Making*. *18* (3): 747-791. doi
   <https://en.m.wikipedia.org/wiki/Doi_%28identifier%29>:
   10.1142/S0219622019500172 <https://doi.org/10.1142%2FS0219622019500172>.
   23. *^ <https://en.m.wikipedia.org/wiki/Uplift_modelling#cite_ref-23>* R.
   Michel, I. Schnakenburg, T. von Martens (2019). „Targeting Uplift“.
   Springer, ISBN
   <https://en.m.wikipedia.org/wiki/International_Standard_Book_Number>
   978-3-030-22625-1
   <https://en.m.wikipedia.org/wiki/Special:BookSources/978-3-030-22625-1>

See alsoEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=13>

   - Lift (data mining)
   <https://en.m.wikipedia.org/wiki/Lift_%28data_mining%29>

External linksEdit
<https://en.m.wikipedia.org/w/index.php?title=Uplift_modelling&action=edit&section=14>

   - Abby Johnson explains how it works in this video broadcast
   <http://videos.smallbusinessnewz.com/2011/01/05/how-uplift-modeling-boosts-marketing-efforts/>
   - Introductory white paper with full references
   <http://www.predictiveanalyticsworld.com/signup-uplift-whitepaper.php>
   - Eric Siegel: Uplift Modeling
   <http://www.predictiveanalyticsworld.com/pdf/YTW03080USEN/Uplift-Modeling-Optimizes-Marketing-Decisions-White-Paper.pdf>
   - User guide for uplift modelling on uplift-modeling.com
   <https://www.uplift-modeling.com/en/latest/user_guide/index.html>

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