32,656 research outputs found
Uplift Modeling with Multiple Treatments and General Response Types
Randomized experiments have been used to assist decision-making in many
areas. They help people select the optimal treatment for the test population
with certain statistical guarantee. However, subjects can show significant
heterogeneity in response to treatments. The problem of customizing treatment
assignment based on subject characteristics is known as uplift modeling,
differential response analysis, or personalized treatment learning in
literature. A key feature for uplift modeling is that the data is unlabeled. It
is impossible to know whether the chosen treatment is optimal for an individual
subject because response under alternative treatments is unobserved. This
presents a challenge to both the training and the evaluation of uplift models.
In this paper we describe how to obtain an unbiased estimate of the key
performance metric of an uplift model, the expected response. We present a new
uplift algorithm which creates a forest of randomized trees. The trees are
built with a splitting criterion designed to directly optimize their uplift
performance based on the proposed evaluation method. Both the evaluation method
and the algorithm apply to arbitrary number of treatments and general response
types. Experimental results on synthetic data and industry-provided data show
that our algorithm leads to significant performance improvement over other
applicable methods
An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests
Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, that can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine and bioinformatics within the past few years.
High dimensional problems are common not only in genetics, but also in some areas of psychological research, where only few subjects can be measured due to time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications, and provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions.
The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application.
Application of the methods is illustrated using freely available implementations in the R system for statistical computing
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