35 research outputs found
Multi-Valued Treatments Uplift Modeling for Continuous Outcomes
Uplift modeling is an application of causal machine learning and offers an assortment of analytical tools to identify likely responders to a particular treatment such as a medical prescription, a political maneuver, or an advertising stimulus. Although several targeted campaigns co-occur (e.g., through different marketing channels), recent literature has primarily examined the effectiveness of a single treatment. To address the practically more pertinent question of which treatment among several options to choose, we develop a prototype that identifies the most effective treatment for each unit of observation and further generalizes to both binary and continuous outcomes to support classification and regression problems. Using real-world data from e-mail merchandising and e-couponing campaigns, we verify our prototype’s financial advantage compared to previous efforts toward the single treatment case
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
A Practically Competitive and Provably Consistent Algorithm for Uplift Modeling
Randomized experiments have been critical tools of decision making for
decades. However, subjects can show significant heterogeneity in response to
treatments in many important applications. Therefore it is not enough to simply
know which treatment is optimal for the entire population. What we need is a
model that correctly customize treatment assignment base on subject
characteristics. The problem of constructing such models from randomized
experiments data is known as Uplift Modeling in the literature. Many algorithms
have been proposed for uplift modeling and some have generated promising
results on various data sets. Yet little is known about the theoretical
properties of these algorithms. In this paper, we propose a new tree-based
ensemble algorithm for uplift modeling. Experiments show that our algorithm can
achieve competitive results on both synthetic and industry-provided data. In
addition, by properly tuning the "node size" parameter, our algorithm is proved
to be consistent under mild regularity conditions. This is the first consistent
algorithm for uplift modeling that we are aware of.Comment: Accepted by 2017 IEEE International Conference on Data Minin
Feature Selection Methods for Uplift Modeling
Uplift modeling is a predictive modeling technique that estimates the
user-level incremental effect of a treatment using machine learning models. It
is often used for targeting promotions and advertisements, as well as for the
personalization of product offerings. In these applications, there are often
hundreds of features available to build such models. Keeping all the features
in a model can be costly and inefficient. Feature selection is an essential
step in the modeling process for multiple reasons: improving the estimation
accuracy by eliminating irrelevant features, accelerating model training and
prediction speed, reducing the monitoring and maintenance workload for feature
data pipeline, and providing better model interpretation and diagnostics
capability. However, feature selection methods for uplift modeling have been
rarely discussed in the literature. Although there are various feature
selection methods for standard machine learning models, we will demonstrate
that those methods are sub-optimal for solving the feature selection problem
for uplift modeling. To address this problem, we introduce a set of feature
selection methods designed specifically for uplift modeling, including both
filter methods and embedded methods. To evaluate the effectiveness of the
proposed feature selection methods, we use different uplift models and measure
the accuracy of each model with a different number of selected features. We use
both synthetic and real data to conduct these experiments. We also implemented
the proposed filter methods in an open source Python package (CausalML)