367 citations · 433 across the 11 of their papers we have counts for
11 papers
The CRITICAL Records Integrated Standardization Pipeline (CRISP): End-to-End Processing of Large-scale Multi-institutional OMOP CDM Data
Xiaolong Luo, Michael Lingzhi Li
While existing critical care EHR datasets such as MIMIC and eICU have enabled significant advances in clinical AI research, the CRITICAL dataset opens new frontiers by providing ex…
Comment on "Generic machine learning inference on heterogeneous treatment effects in randomized experiments."
Kosuke Imai, Michael Lingzhi Li
We analyze the split-sample robust inference (SSRI) methodology proposed by Chernozhukov, Demirer, Duflo, and Fernandez-Val (CDDF) for quantifying uncertainty in heterogeneous trea…
Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation
Michael Lingzhi Li, Shixiang Zhu
Many decision-support systems recommend actions by optimizing measurable objectives, even when a human decision-maker retains final authority and considers additional criteria that…
Neyman Meets Causal Machine Learning: Experimental Evaluation of Individualized Treatment Rules
Michael Lingzhi Li, Kosuke Imai
A century ago, Neyman showed how to evaluate the efficacy of treatment using a randomized experiment under a minimal set of assumptions. This classical repeated sampling framework…
Cramming Contextual Bandits for On-policy Statistical Evaluation
Zeyang Jia, Kosuke Imai, Michael Lingzhi Li
We introduce the cram method as a general statistical framework for evaluating the final learned policy from a multi-armed contextual bandit algorithm, using the dataset generated…
Statistical Performance Guarantee for Subgroup Identification with Generic Machine Learning
Michael Lingzhi Li, Kosuke Imai
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…
Benchmarking Large Language Models on CMExam -- A Comprehensive Chinese Medical Exam Dataset
Junling Liu, Peilin Zhou, Yining Hua +8
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the l…
Distributionally Robust Causal Inference with Observational Data
Dimitris Bertsimas, Kosuke Imai, Michael Lingzhi Li
We consider the estimation of average treatment effects in observational studies and propose a new framework of robust causal inference with unobserved confounders. Our approach is…
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
Kosuke Imai, Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fai…
Integrated multimodal artificial intelligence framework for healthcare applications
Luis R. Soenksen, Yu Ma, Cynthia Zeng +7
Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities…
Experimental Evaluation of Individualized Treatment Rules
Kosuke Imai, Michael Lingzhi Li
The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empiri…