
Matching Bounds: How Choice of Matching Algorithm Impacts Treatment Effects Estimates and What to Do about It
Different matches on the same data may produce different treatment effec...
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Bandits for BMO Functions
We study the bandit problem where the underlying expected reward is a Bo...
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Generalized Optimal Sparse Decision Trees
Decision tree optimization is notoriously difficult from a computational...
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In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
In recent years, academics and investigative journalists have criticized...
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PULSE: SelfSupervised Photo Upsampling via Latent Space Exploration of Generative Models
The primary aim of singleimage superresolution is to construct a high...
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Adaptive Hyperbox Matching for Interpretable Individualized Treatment Effect Estimation
We propose a matching method for observational data that matches units w...
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AlmostMatchingExactly for Treatment Effect Estimation under Network Interference
We propose a matching method that recovers direct treatment effects from...
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Concept Whitening for Interpretable Image Recognition
What does a neural network encode about a concept as we traverse through...
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A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning
The Rashomon effect occurs when many different explanations exist for th...
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Reducing Exploration of Dying Arms in Mortal Bandits
Mortal bandits have proven to be extremely useful for providing news art...
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Interpretable AlmostMatchingExactly With Instrumental Variables
Uncertainty in the estimation of the causal effect in observational stud...
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Interpretable Image Recognition with Hierarchical Prototypes
Vision models are interpretable when they classify objects on the basis ...
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The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis
Despite the widespread usage of machine learning throughout organization...
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Optimal Sparse Decision Trees
Decision tree algorithms have been among the most popular algorithms for...
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A Practical Bandit Method with Advantages in Neural Network Tuning
Stochastic bandit algorithms can be used for challenging nonconvex opti...
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Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models
Variable importance is central to scientific studies, including the soci...
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Hypothesis Tests That Are Robust to Choice of Matching Method
A vast number of causal inference studies test hypotheses on treatment e...
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An Interpretable Model with Globally Consistent Explanations for Credit Risk
We propose a possible solution to a public challenge posed by the Fair I...
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Please Stop Explaining Black Box Models for High Stakes Decisions
There are black box models now being used for high stakes decisionmakin...
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MALTS: Matching After Learning to Stretch
We introduce a flexible framework for matching in causal inference that ...
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Shall I Compare Thee to a MachineWritten Sonnet? An Approach to Algorithmic Sonnet Generation
We provide code that produces beautiful poetry. Our sonnetgeneration al...
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The age of secrecy and unfairness in recidivism prediction
In our current society, secret algorithms make important decisions about...
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Bayesian Patchworks: An Approach to CaseBased Reasoning
Doctors often rely on their past experience in order to diagnose patient...
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This looks like that: deep learning for interpretable image recognition
When we are faced with challenging image classification tasks, we often ...
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CollapsingFastLargeAlmostMatchingExactly: A Matching Method for Causal Inference
We aim to create the highest possible quality of treatmentcontrol match...
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New Techniques for Preserving Global Structure and Denoising with Low Information Loss in SingleImage SuperResolution
This work identifies and addresses two important technical challenges in...
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A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results
Inference is the process of using facts we know to learn about facts we ...
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A Minimax Surrogate Loss Approach to Conditional Difference Estimation
We present a new machine learning approach to estimate personalized trea...
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Direct Learning to Rank and Rerank
Learningtorank techniques have proven to be extremely useful for prior...
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Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective
There are serious drawbacks to many current variable importance (VI) met...
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Dimension Reduction for Robust Covariate Shift Correction
In the covariate shift learning scenario, the training and test covariat...
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Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect
We introduce a novel generative model for interpretable subgroup analysi...
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Deep Learning for CaseBased Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Deep neural networks are widely used for classification. These deep mode...
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FLAME: A Fast Largescale Almost Matching Exactly Approach to Causal Inference
A classical problem in causal inference is that of matching, where treat...
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Learning Certifiably Optimal Rule Lists for Categorical Data
We present the design and implementation of a custom discrete optimizati...
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Learning CostEffective Treatment Regimes using Markov Decision Processes
Decision makers, such as doctors and judges, make crucial decisions such...
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Learning Optimized Risk Scores on LargeScale Datasets
Risk scores are simple classification models that let users quickly asse...
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Interpretable Machine Learning Models for the Digital Clock Drawing Test
The Clock Drawing Test (CDT) is a rapid, inexpensive, and popular neurop...
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Scalable Bayesian Rule Lists
We present an algorithm for building probabilistic rule lists that is tw...
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Learning Optimized Or's of And's
Or's of And's (OA) models are comprised of a small number of disjunction...
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Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
We aim to produce predictive models that are not only accurate, but are ...
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Cascaded High Dimensional Histograms: A Generative Approach to Density Estimation
We present tree and list structured density estimation methods for hig...
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Causal Falling Rule Lists
A causal falling rule list (CFRL) is a sequence of ifthen rules that sp...
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CRAFT: ClusteRspecific Assorted Feature selecTion
We present a framework for clustering with clusterspecific feature sele...
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Regulating Greed Over Time
In retail, there are predictable yet dramatic timedependent patterns in...
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Modeling Recovery Curves With Application to Prostatectomy
We propose a Bayesian model that predicts recovery curves based on infor...
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Interpretable Classification Models for Recidivism Prediction
We investigate a longdebated question, which is how to create predictiv...
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The Bayesian Case Model: A Generative Approach for CaseBased Reasoning and Prototype Classification
We present the Bayesian Case Model (BCM), a general framework for Bayesi...
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Supersparse Linear Integer Models for Optimized Medical Scoring Systems
Scoring systems are linear classification models that only require users...
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Falling Rule Lists
Falling rule lists are classification models consisting of an ordered li...
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Cynthia Rudin
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Associate Professor of Computer Science and Electrical and Computer Engineering PI, Prediction Analysis Lab at Duke University