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Found 16 papers, 3 papers with code
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
In this paper, we demonstrate that Neyman's methodology can also be used to experimentally evaluate the efficacy of individualized treatment rules (ITRs), which are derived by modern causal machine learning algorithms.
The Cram Method for Efficient Simultaneous Learning and Evaluation
Our extensive simulation studies show that, when compared to sample-splitting, cramming reduces the evaluation standard error by more than 40% while improving the performance of learned policy.
Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
Across a wide array of disciplines, many researchers use machine learning (ML) algorithms to identify a subgroup of individuals who are likely to benefit from a treatment the most (``exceptional responders'') or those who are harmed by it.
Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset
To the best of our knowledge, CMExam is the first Chinese medical exam dataset to provide comprehensive medical annotations.
Distributionally Robust Causal Inference with Observational Data
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders.
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
In addition, we develop nonparametric tests of treatment effect homogeneity across groups, and rank-consistency of within-group average treatment effects.
Holistic Deep Learning
This paper presents a novel holistic deep learning framework that simultaneously addresses the challenges of vulnerability to input perturbations, overparametrization, and performance instability from different train-validation splits.
Stochastic Cutting Planes for Data-Driven Optimization
We introduce a stochastic version of the cutting-plane method for a large class of data-driven Mixed-Integer Nonlinear Optimization (MINLO) problems.
Where to locate COVID-19 mass vaccination facilities?
As compared to benchmarks based on demographic and epidemiological information, the proposed optimization approach increases the effectiveness of the vaccination campaign by an estimated $20\%$, saving an extra $4000$ extra lives in the United States over a three-month period.
From predictions to prescriptions: A data-driven response to COVID-19
Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact.
A Hierarchy of Graph Neural Networks Based on Learnable Local Features
We derive theoretical results about the discriminative power and feature representation capabilities of each class.
Fast Exact Matrix Completion: A Unified Optimization Framework for Matrix Completion
We formulate the problem of matrix completion with and without side information as a non-convex optimization problem.
Experimental Evaluation of Individualized Treatment Rules
We extend our methodology to a common setting, in which the same experimental data is used to both estimate and evaluate ITRs.
Duration-of-Stay Storage Assignment under Uncertainty
Optimizing storage assignment is a central problem in warehousing.
Scalable Holistic Linear Regression
We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016).
Interpretable Matrix Completion: A Discrete Optimization Approach
We consider the problem of matrix completion on an $n \times m$ matrix.
