Lg ph d_slides_vfinal
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The document outlines Leo Guelman's PhD thesis on developing improved statistical and algorithmic methods for personalized treatment learning (PTL) models, with applications to insurance. It introduces PTL, which aims to select the optimal treatment for each individual based on their characteristics. The thesis contributes novel PTL methods like uplift random forests and causal conditional inference trees, and shows they outperform existing approaches in simulations. It also applies PTL to optimize insurance marketing strategies using experimental data from a large insurer.
![Motivation
In the context of causal learning, the main interest has been in
identifying the Average Treatment Effect (ATE):
ATE = E[Y |A = 1] − E[Y |A = 0].
In many important settings, subjects can show significant
heterogeneity in response to actions/treatments – i.e., what
works for one subject may not work for another. Here the ATE is
less relevant.
The objective becomes to select the optimal action or
“treatment” for each subject based on individual characteristics.
Introduction
Current Approaches
Personalized Medicine
Goal
“Providing meaningful improved health outcomes for patients by
delivering the right drug at the right dose at the right time.”
How Do We Apply Personalized Medicine?
Learn individualized treatment rules: tailor treatments based
on patient characteristics.
Concepts & Tools
Symptoms
Demographics
Disease history
Biomarkers
Imaging
Bioinformatics
Pharmacogenomics
Concepts & Tools
Symptoms
Demographics
Disease history
Biomarkers
Imaging
Bioinformatics
Pharmacogenomics
When Do We Apply Personalized Medicine?
Single-Decision Setup.
Multi-Decision Setup.
Optimal defined as the treatment that maximizes the probability
of a desirable outcome.
We call the task of learning the optimal personalized treatment
personalized treatment learning (PTL).
4 / 22](https://image.slidesharecdn.com/lgphdslidesvfinal-150304110239-conversion-gate01/85/Lg-ph-d_slides_vfinal-4-320.jpg)
![Problem Formulation
At the most granular level, the personalized treatment
effect (PTE) is a comparison between Y (1) and Y (0) on the
same subject. Usually,
Y (1) − Y (0) ∀ = {1, . . . , L}.
But this quantity is unobserved...
In practice, the best we can do is to estimate the PTE by
conditioning on subjects with profile X = x.
Thus, we define the PTE by
τ(x) = E[Y (1) − Y (0)|X = x]
= E[Y |X = x, A = 1] − E[Y |X = x, A = 0].
6 / 22](https://image.slidesharecdn.com/lgphdslidesvfinal-150304110239-conversion-gate01/85/Lg-ph-d_slides_vfinal-6-320.jpg)
![Problem Formulation
A personalized treatment rule H is a map from the space of
baseline covariates X to the space of treatments A,
H(X) : Rp
→ {0, 1}.
An optimal treatment rule is one that maximizes the expected
outcome, E[Y (H(X))], if the personalized treatment rule is
implemented for the whole population.
Since Y is binary, this expectation has a probabilistic interpretation.
That is, E[Y (H(X))] = P Y (H(X)) = 1 and thus τ(x) ∈ [−1, 1].
Assuming all treatments cost the same, the optimal personalized
treatment rule H∗
= argmaxH E[Y (H(X))] for a subject with
covariates X = x is given by
H∗
=
1 if τ(x) > 0
0 otherwise.
7 / 22](https://image.slidesharecdn.com/lgphdslidesvfinal-150304110239-conversion-gate01/85/Lg-ph-d_slides_vfinal-7-320.jpg)
![The Simplest Approach to PTE Estimation
1 Estimate E[Y |X, A = 1] using the treated subjects only.
2 Estimate E[Y |X, A = 0] using the control subjects only.
3 An estimate of the PTE for a subject with predictors X = x is
ˆτ(x) = ( ˆY |X = x , A = 1) − ( ˆY |X = x , A = 0).
Pros:
Any conventional statistical or algorithmic binary classification
method may serve to fit the models.
Cons:
Method aims to predict the wrong target: it emphasizes the
prediction accuracy on the response under each treatment, not
the accuracy in estimating the change in the response
caused by the treatment.
Relevant predictors for Y under each treatment are usually
different from relevant PTE predictors.
As a result, it tends to perform poorly in practice.
8 / 22](https://image.slidesharecdn.com/lgphdslidesvfinal-150304110239-conversion-gate01/85/Lg-ph-d_slides_vfinal-8-320.jpg)
![Manuscripts Linked to Thesis
[1] Guelman, L. (2014). uplift: Uplift modeling. R package version 0.3.5.
[2] Guelman, L. and Guill´en, M. (2014). A causal inference approach to measure price
elasticity in automobile insurance. Expert Systems with Applications, 41(2):387–396.
[3] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A. M. (2014). A survey of personalized
treatment models for pricing strategies in insurance. Insurance: Mathematics and
Economics, 58:68–76.
[4] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A. M. (2014). Uplift random forests.
Cybernetics & Systems. Accepted.
[5] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A. M. (2014). A decision support
framework to implement optimal personalized marketing interventions. Decision
Support Systems, 72: 24–32.
[6] Guelman, L. (2012). Gradient boosting trees for auto insurance loss cost modeling
and prediction. Expert Systems with Applications, 39(3):3659–3667.
[7] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A. M. (2012). Random forests for uplift
modeling: An insurance customer retention case. In Engemann, K. J., Lafuente, A. M.
G., and Merig´o, J. M., editors, Modeling and Simulation in Engineering, Economics
and Management, pages 123–133. Springer Berlin Heidelberg, New York, NY.
20 / 22](https://image.slidesharecdn.com/lgphdslidesvfinal-150304110239-conversion-gate01/85/Lg-ph-d_slides_vfinal-20-320.jpg)
![Talks Linked to Thesis
[1] Guill´en, M. and Guelman, L. (2014). “New trends in predictive modelling - the uplift models success story”.
R in Insurance Conference, London, UK. (July 14, 2014).
[2] Guelman, L. and Guill´en, M. (2014). “Actionable predictive learning for insurance profit maximization”.
Casualty Actuarial Society, Ratemaking and Product Management Seminar, Washington, D.C., USA (April 1,
2014).
[3] Guelman, L. (2013). “An introduction to causal learning with applications to price elasticity modeling in
Casualty insurance”. University of Barcelona, UB Economics Seminars, Barcelona, Spain (November 28, 2013).
[4] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A.M. (2013). “Evaluating customer loyalty with advanced uplift
models”. APRIA, Annual Conference, New York, USA (July 28-31, 2013).
[5] Guill´en, M., Guelman, L., M. and P´erez-Mar´ın, A.M. (2013). “Customer retention and price elasticity. Are
motor insurance policies homogeneous with respect to loyalty?”. 2013 Astin colloquium, The Hague, Netherlands
(May 21-24, 2013).
[6] Guelman, L. and Lee, S. (2013). “Balancing robust statistics - gradient boosting”. Casualty Actuarial Society,
Ratemaking and Product Management Seminar, Los Angeles, USA (March 12-13, 2013).
[7] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A.M. (2012). “Random forests for uplift modeling: an insurance
customer retention case”. Association of Modeling and Simulation in Enterprise (AMSE) - International Conference
on Modeling and Simulation, New York, USA (May 30-June 1, 2012). – Outstanding Scholarly Research
Contribution Award, AMSE.
[8] Guelman, L. and Lee, S. (2012). “Balancing robust statistics and data mining in ratemaking: gradient boosting
modeling”. Casualty Actuarial Society, Ratemaking and Product Management Seminar, Philadelphia, USA (March
20-21, 2012).
21 / 22](https://image.slidesharecdn.com/lgphdslidesvfinal-150304110239-conversion-gate01/85/Lg-ph-d_slides_vfinal-21-320.jpg)
Lg ph d_slides_vfinal
- 1. Optimal Personalized Treatment Learning Models with Insurance Applications PhD Student: Leo Guelman Advisor: Dr. Montserrat Guill´en PhD in Economics University of Barcelona March 2, 2015 1 / 22
- 2. Outline 1 Motivation 2 Problem formulation 3 Objectives 4 Challenges 5 Contributions 6 Limitations and future work 2 / 22
- 3. Motivation Predictive learning has been established as a core strategic capability in many scientific disciplines and industries. Common goal: Predict the value of a response variable using a collection of covariates or “predictors” under static conditions. From Predictive to Causal Modeling • Predictive Modeling has been established as a core strategic capability of many top insurers. • Common goal: to predict a response variable using a collection of attributes under static conditions — i.e., assumes “business as usual” conditions. • Causal Modeling goes one step further: the interest is in estimating/ predicting the response under changing conditions — e.g., induced by alternative actions or “treatments”. 3 X YCovariates Response XCovariates Potential responses Y (1) Y (2) Actions A = 0 A = 1 Causal learning goes one step further: the interest is in estimating/predicting the response under changing conditions – e.g., induced by alternative actions or “treatments”. From Predictive to Causal Modeling • Predictive Modeling has been established as a core strategic capability of many top insurers. • Common goal: to predict a response variable using a collection of attributes under static conditions — i.e., assumes “business as usual” conditions. • Causal Modeling goes one step further: the interest is in estimating/ predicting the response under changing conditions — e.g., induced by alternative actions or “treatments”. 3 X YCovariates Response XCovariates Potential responses Y (1) Y (2) Actions A = 0 A = 1 3 / 22
- 4. Motivation In the context of causal learning, the main interest has been in identifying the Average Treatment Effect (ATE): ATE = E[Y |A = 1] − E[Y |A = 0]. In many important settings, subjects can show significant heterogeneity in response to actions/treatments – i.e., what works for one subject may not work for another. Here the ATE is less relevant. The objective becomes to select the optimal action or “treatment” for each subject based on individual characteristics. Introduction Current Approaches Personalized Medicine Goal “Providing meaningful improved health outcomes for patients by delivering the right drug at the right dose at the right time.” How Do We Apply Personalized Medicine? Learn individualized treatment rules: tailor treatments based on patient characteristics. Concepts & Tools Symptoms Demographics Disease history Biomarkers Imaging Bioinformatics Pharmacogenomics Concepts & Tools Symptoms Demographics Disease history Biomarkers Imaging Bioinformatics Pharmacogenomics When Do We Apply Personalized Medicine? Single-Decision Setup. Multi-Decision Setup. Optimal defined as the treatment that maximizes the probability of a desirable outcome. We call the task of learning the optimal personalized treatment personalized treatment learning (PTL). 4 / 22
- 5. Problem Formulation Assume a randomized experiment – i.e., subjects are randomly assigned to two treatments, denoted by A ∈ {0, 1}. Let Y (a) ∈ {0, 1} denote a binary potential response of a subject if assigned to treatment A = a, a = {0, 1}. So the observed response is Y = AY (1) + (1 − A)Y (0). Subjects are characterized by a p-dimensional vector of baseline predictors X = (X1, . . . , Xp) . Data consist of L i.i.d. realizations of (Y , A, X), {(Y , A , X ), = 1, . . . , L}. 5 / 22
- 6. Problem Formulation At the most granular level, the personalized treatment effect (PTE) is a comparison between Y (1) and Y (0) on the same subject. Usually, Y (1) − Y (0) ∀ = {1, . . . , L}. But this quantity is unobserved... In practice, the best we can do is to estimate the PTE by conditioning on subjects with profile X = x. Thus, we define the PTE by τ(x) = E[Y (1) − Y (0)|X = x] = E[Y |X = x, A = 1] − E[Y |X = x, A = 0]. 6 / 22
- 7. Problem Formulation A personalized treatment rule H is a map from the space of baseline covariates X to the space of treatments A, H(X) : Rp → {0, 1}. An optimal treatment rule is one that maximizes the expected outcome, E[Y (H(X))], if the personalized treatment rule is implemented for the whole population. Since Y is binary, this expectation has a probabilistic interpretation. That is, E[Y (H(X))] = P Y (H(X)) = 1 and thus τ(x) ∈ [−1, 1]. Assuming all treatments cost the same, the optimal personalized treatment rule H∗ = argmaxH E[Y (H(X))] for a subject with covariates X = x is given by H∗ = 1 if τ(x) > 0 0 otherwise. 7 / 22
- 8. The Simplest Approach to PTE Estimation 1 Estimate E[Y |X, A = 1] using the treated subjects only. 2 Estimate E[Y |X, A = 0] using the control subjects only. 3 An estimate of the PTE for a subject with predictors X = x is ˆτ(x) = ( ˆY |X = x , A = 1) − ( ˆY |X = x , A = 0). Pros: Any conventional statistical or algorithmic binary classification method may serve to fit the models. Cons: Method aims to predict the wrong target: it emphasizes the prediction accuracy on the response under each treatment, not the accuracy in estimating the change in the response caused by the treatment. Relevant predictors for Y under each treatment are usually different from relevant PTE predictors. As a result, it tends to perform poorly in practice. 8 / 22
- 9. Objectives Formalize personalized treatment learning (PTL) as a new branch of statistical learning. Create the first comprehensive systematic review of the existing PTL methods (Tian and Tibshirani, 2014; Radcliffe and Surry, 2011; Ja´skowski and Jaroszewicz, 2012; Su et al., 2009; Qian and Murphy, 2011; Zhao et al., 2012; Rubin and Waterman, 2006; Larsen, 2009; Imai and Ratkovic, 2013, and others). Build improved statistical/algorithmic methods for estimating, selecting and assessing PTL models. Introduce PTL models to insurance applications. Build open source software implementing our proposed methods for fitting PTL models, as well as the existing ones, and make it freely available for academia/industry. 9 / 22
- 10. Key Challenges The fundamental problem of PTL models: The quantity we are trying to predict (i.e., the optimal personalized treatment) is unknown on a given training data set. Size of main effects relative to treatment heterogeneity effects: The magnitude of the variability in the response due to the treatment heterogeneity effects is usually much smaller than the variability due to the main effects. Overfitting: The risk of overfitting increases markedly in PTL models compared to conventional predictive learning problems. Model selection and assessment: Methods for variable selection and model selection/assessment used in conventional predictive learning problems need to be redefined in the context of PTL models. 10 / 22
- 11. Contributions Introduced and formalized the concept of personalized treatment learning (PTL) within a causal inference framework, and described its relevance to a wide variety of fields ranging from economics to medicine (Ch. 1-2). Provided the first comprehensive description of the existing PTL methods (Ch. 3) and proposed two novel methods – namely, uplift random forests (Ch. 4) and causal conditional inference trees (Ch. 5). Our proposal outperforms the existing methods in an extensive numerical simulation study (Ch. 6). PTL models require not only developing new estimation methods, but also new methods for assessing model performance. We formalized the concept of the Qini curve and the Qini coefficient, and discussed general useful methods for model assessment and selection for PTL models (Ch. 7). 11 / 22
- 12. Contributions We described the relevance of PTL models to insurance marketing, and illustrated two applications to optimize client retention and cross-selling using experimental data from a large Canadian insurer (Ch. 8). We presented a novel approach to measuring price-elasticity and economic price optimization in non-life insurance based on PTL modeling principles in the context of observational data (Ch. 9). Selecting the optimal personalized treatment in insurance also requires consideration of the expected losses under treatment alternatives. We described an unprecedented application of gradient boosting models to estimate loss cost in non-life insurance, with key advantages over the conventional generalized linear model approach (Ch. 10). We implemented most of the statistical methods and algorithms described in this thesis in a package named uplift (Guelman, 2014), which is now freely available from the CRAN (Comprehensive R Archive Network) repository under the R statistical computing environment (Ch. 11). 12 / 22
- 13. PTL with Experimental Data An Application to Insurance Cross-Sell Optimization Cross-sell rates by group Treatment Control Purchased home policy = N 30,184 3,322 Purchased home policy = Y 789 75 Cross-sell rate 2.55% 2.21% The average treatment effect (ATE) is 0.34% (2.55% − 2.21%) which is NOT statistically significant (P value = 0.23). Cross-sell rate by PTL model decile Deciles 0.03 0.06 q q q q q q q q q q q q q q q q q q q q Profitable targets Not profitable targets 0 2 4 6 8 10 12 1 2 3 4 5 6 7 8 9 10 Decile Cross−sellrate(%) Group q qTreatment Control Prototype causal conditional inference tree 0.34% 0.05% prodCnt ≤ 1 0.65% 1.22% adjBranch 0.38% 0.67% 2.52% age ≤ 45 -1.2% 0.32% xdate ≤ 2 -1.6% age ≤ 42 -0.2% The tree-based procedure identifies a subgroup of clients with significant positive impact from the cross-sell activity (PTE = 2.52%). 13 / 22
- 14. Causal Conditional Inference Tree - Pseudocode Algorithm 1 Causal conditional inference tree 1: for each terminal node do 2: Test the global null hypothesis H0 of no interaction effect between the treatment A and any of the p predictors at a level of significance α based on a permutation test (Strasser and Weber, 1999) 3: if the null hypothesis H0 cannot be rejected then 4: Stop 5: else 6: Select the j∗ th predictor Xj∗ with the strongest interaction effect (i.e., the one with the smallest adjusted P value) 7: Choose a partition Ω∗ of the covariate Xj∗ in two disjoint sets M ⊂ Xj∗ and Xj∗ M based on the G2 (Ω) split criterion 8: end if 9: end for G2 (Ω) = (L − 4){ Left Node ( ¯YnL (1) − ¯YnL (0)) − Right Node ( ¯YnR (1) − ¯YnR (0))}2 ˆσ2{1/LnL (1) + 1/LnL (0) + 1/LnR (1) + 1/LnR (0)} 14 / 22
- 15. Numerical simulations – Performance comparison “Strong” treatment heterogeneity effects Simulation scenarios are obtained by varying: (i) the strength of the main effects relative to the treatment heterogeneity effects, (ii) the correlation among the covariates, (iii) the magnitude of the random noise. qqqqqq q q qqqq qqq qqq q q qqqq q qqqqq qqqqqqqq qqqq q qqqqq qqqqqqqq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q qq q q q qq q qq q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q qq q qq q q q q q q q q q q q q Scenario 1 Scenario 2 Scenario 3 Scenario 4 0.00 0.25 0.50 0.75 0.00 0.25 0.50 0.75 0.00 0.25 0.50 0.75 −0.3 0.0 0.3 0.6 l2svm ccif upliftRF dsm dsm−RF mom−RF mom mcm cknn mcm−RF int int−RF ccif upliftRF dsm−RF mom−RF cknn mcm−RF int−RF l2svm mom mcm dsm int ccif l2svm upliftRF mom−RF dsm−RF mom mcm dsm mcm−RF cknn int−RF int ccif upliftRF dsm−RF mom−RF mcm−RF cknn int−RF mom mcm l2svm dsm int Method Spearman'srankcorrelationcoefficient 15 / 22
- 16. Numerical simulations – Performance comparison “Weak” treatment heterogeneity effects q q q q q qq q q q q q q q q q q q q q q q q q q q q q qq q q q qq q qq q qq q qq qq q q q q q q q q q q q q q qq q q q q q qq q q q q q q qq q q q q q q q q q q q q q q q q q q qq q q q q q q q q q q q qq q q q q q q q q q q q qq q q q qqq q q q q q q q q q q qq q q q qqq q q q q q q q q q q q q q q q qq q q q q qq q q q q q q q q q q q q Scenario 5 Scenario 6 Scenario 7 Scenario 8 0.00 0.25 0.50 0.75 −0.5 0.0 0.5 0.00 0.25 0.50 0.75 −0.25 0.00 0.25 0.50 dsm ccif upliftRF l2svm dsm−RF mom−RF int cknn mcm−RF int−RF mom mcm ccif upliftRF dsm−RF dsm mom−RF cknn mcm−RF int−RF l2svm mom mcm int dsm ccif upliftRF dsm−RF mom−RF mcm−RF cknn l2svm int−RF int mom mcm ccif upliftRF dsm−RF mom−RF mcm−RF cknn dsm int−RF l2svm mom mcm int Method Spearman'srankcorrelationcoefficient 16 / 22
- 17. PTL with Observational Data An application to Auto Insurance Economic Price Optimization Background Objective: Determine the policyholder-level premium (playing the role of the treatment) that maximizes the expected profitability of an existing insurance portfolio, subject to a fixed overall retention rate. Requires estimating the expected client retention outcome under alternative insurance rates (price elasticity), and loss cost. Clients were historically exposed to rating actions based on a non-random assignment mechanism, which requires designing an observational study. Maximize an expected profit function Max Z a∀ ∀a ∀ ∀a Z a P (1 + RCa)(1 − ˆLR a)(1 − ˆˆr a) subject to a retention constraint ∀a Z a = 1 ∀ Z a ∈ {0, 1} ∀ a Z a ˆˆr a/L ≤ α. q Current state Efficie ntfrontie r A B C 0 10 20 30 0.90 0.92 0.94 0.96 Retention rate (1 − α) Expectedprofit(%) 17 / 22
- 18. uplift Package Highlights First R package implementing PTL models Exploratory Data Analysis (EDA) tools customized for PTL models Check balance of covariates (checkBalance) Univariate uplift analysis (explore) Preliminary variable screening (niv) Estimating personalized treatment effects Causal conditional inference forests (ccif) Uplift random forests (upliftRF) Modified covariate method (tian_transf) Modified outcome method (rvtu) Uplift k-nearest neighbor (upliftKNN) Performance assessment for PTL models Uplift by decile (performance) Qini curve and Qini-coefficient (qini) Other functionality Profiling PTL models (modelProfile) Monte-Carlo simulations (sim_pte) 18 / 22
- 19. Fitting a CCIF using uplift ccif implements recursive partitioning in a causal conditional inference framework. fit <- ccif(formula = Y ~ trt(A) + X1 + X2 + X3, data = mydata, ntree = 1000, split_method = "Int", distribution = approximate (B=999), pvalue = 0.05, verbose = TRUE) Table: Some ccif options ccif argument Description mtry Number of variables to be tested in each node ntree Number of trees in the forest split_method Split criteria: "KL", "ED", "Int" or "L1" interaction.depth The maximum depth of variable interactions pvalue Maximum acceptable p-value required to make a split bonferroni Apply Bonferroni adjustment to pvalue minsplit Minimum number of obs. for a split to be attempted ... Additional args. passed to coin::independence_test. 19 / 22
- 20. Manuscripts Linked to Thesis [1] Guelman, L. (2014). uplift: Uplift modeling. R package version 0.3.5. [2] Guelman, L. and Guill´en, M. (2014). A causal inference approach to measure price elasticity in automobile insurance. Expert Systems with Applications, 41(2):387–396. [3] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A. M. (2014). A survey of personalized treatment models for pricing strategies in insurance. Insurance: Mathematics and Economics, 58:68–76. [4] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A. M. (2014). Uplift random forests. Cybernetics & Systems. Accepted. [5] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A. M. (2014). A decision support framework to implement optimal personalized marketing interventions. Decision Support Systems, 72: 24–32. [6] Guelman, L. (2012). Gradient boosting trees for auto insurance loss cost modeling and prediction. Expert Systems with Applications, 39(3):3659–3667. [7] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A. M. (2012). Random forests for uplift modeling: An insurance customer retention case. In Engemann, K. J., Lafuente, A. M. G., and Merig´o, J. M., editors, Modeling and Simulation in Engineering, Economics and Management, pages 123–133. Springer Berlin Heidelberg, New York, NY. 20 / 22
- 21. Talks Linked to Thesis [1] Guill´en, M. and Guelman, L. (2014). “New trends in predictive modelling - the uplift models success story”. R in Insurance Conference, London, UK. (July 14, 2014). [2] Guelman, L. and Guill´en, M. (2014). “Actionable predictive learning for insurance profit maximization”. Casualty Actuarial Society, Ratemaking and Product Management Seminar, Washington, D.C., USA (April 1, 2014). [3] Guelman, L. (2013). “An introduction to causal learning with applications to price elasticity modeling in Casualty insurance”. University of Barcelona, UB Economics Seminars, Barcelona, Spain (November 28, 2013). [4] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A.M. (2013). “Evaluating customer loyalty with advanced uplift models”. APRIA, Annual Conference, New York, USA (July 28-31, 2013). [5] Guill´en, M., Guelman, L., M. and P´erez-Mar´ın, A.M. (2013). “Customer retention and price elasticity. Are motor insurance policies homogeneous with respect to loyalty?”. 2013 Astin colloquium, The Hague, Netherlands (May 21-24, 2013). [6] Guelman, L. and Lee, S. (2013). “Balancing robust statistics - gradient boosting”. Casualty Actuarial Society, Ratemaking and Product Management Seminar, Los Angeles, USA (March 12-13, 2013). [7] Guelman, L., Guill´en, M. and P´erez-Mar´ın, A.M. (2012). “Random forests for uplift modeling: an insurance customer retention case”. Association of Modeling and Simulation in Enterprise (AMSE) - International Conference on Modeling and Simulation, New York, USA (May 30-June 1, 2012). – Outstanding Scholarly Research Contribution Award, AMSE. [8] Guelman, L. and Lee, S. (2012). “Balancing robust statistics and data mining in ratemaking: gradient boosting modeling”. Casualty Actuarial Society, Ratemaking and Product Management Seminar, Philadelphia, USA (March 20-21, 2012). 21 / 22
- 22. Limitations and Future Work 1 Extensions to multi-category and continuous treatment settings. 2 Extensions to continuous uncensored and survival responses. 3 Extensions to dynamic treatment regimes: treatment type may be repeatedly adjusted according to an ongoing individual response. 4 Absolute vs. relative treatment effects – in some settings, defining the treatment effect in terms of the ratio of the expected responses under alternative treatment conditions, instead of the difference between the expected responses may be more appropriate. 22 / 22