2 research outputs found
A Minimax Surrogate Loss Approach to Conditional Difference Estimation
We present a new machine learning approach to estimate personalized treatment
effects in the classical potential outcomes framework with binary outcomes. To
overcome the problem that both treatment and control outcomes for the same unit
are required for supervised learning, we propose surrogate loss functions that
incorporate both treatment and control data. The new surrogates yield tighter
bounds than the sum of losses for treatment and control groups. A specific
choice of loss function, namely a type of hinge loss, yields a minimax support
vector machine formulation. The resulting optimization problem requires the
solution to only a single convex optimization problem, incorporating both
treatment and control units, and it enables the kernel trick to be used to
handle nonlinear (also non-parametric) estimation. Statistical learning bounds
are also presented for the framework, and experimental results.Comment: 33 pages, 12 figure
Learning Certifiably Optimal Rule Lists for Categorical Data
We present the design and implementation of a custom discrete optimization
technique for building rule lists over a categorical feature space. Our
algorithm produces rule lists with optimal training performance, according to
the regularized empirical risk, with a certificate of optimality. By leveraging
algorithmic bounds, efficient data structures, and computational reuse, we
achieve several orders of magnitude speedup in time and a massive reduction of
memory consumption. We demonstrate that our approach produces optimal rule
lists on practical problems in seconds. Our results indicate that it is
possible to construct optimal sparse rule lists that are approximately as
accurate as the COMPAS proprietary risk prediction tool on data from Broward
County, Florida, but that are completely interpretable. This framework is a
novel alternative to CART and other decision tree methods for interpretable
modeling.Comment: A short version of this work appeared in KDD '17 as "Learning
Certifiably Optimal Rule Lists