2 citations · 4 across the 10 of their papers we have counts for
10 papers
Strategic Feature Selection
Jivat Neet Kaur, Pratik Patil, Divya Shanmugam +6
When algorithmic predictors inform resource allocation in high-stakes domains such as healthcare, these predictors must account for strategic manipulation of input features. The ty…
Reliable agent engineering should integrate machine-compatible organizational principles
R. Patrick Xian, Garry A. Gabison, Ahmed Alaa +2
As AI agents built on large language models (LLMs) become increasingly embedded in society, issues of coordination, control, delegation, and accountability are entangled with conce…
Hybrid Meta-learners for Estimating Heterogeneous Treatment Effects
Zhongyuan Liang, Lars van der Laan, Ahmed Alaa
Estimating conditional average treatment effects (CATE) from observational data involves modeling decisions that differ from supervised learning, particularly concerning how to reg…
Conformal Prediction Sets with Improved Conditional Coverage using Trust Scores
Jivat Neet Kaur, Michael I. Jordan, Ahmed Alaa
Standard conformal prediction offers a marginal guarantee on coverage, but for prediction sets to be truly useful, they should ideally ensure coverage conditional on each test poin…
Generalized Venn and Venn-Abers Calibration with Applications in Conformal Prediction
Lars van der Laan, Ahmed Alaa
Ensuring model calibration is critical for reliable prediction, yet popular distribution-free methods such as histogram binning and isotonic regression offer only asymptotic guaran…
Veridical Data Science for Medical Foundation Models
Ahmed Alaa, Bin Yu
The advent of foundation models (FMs) such as large language models (LLMs) has led to a cultural shift in data science, both in medicine and beyond. This shift involves moving away…
Large Language Models as Co-Pilots for Causal Inference in Medical Studies
Ahmed Alaa, Rachael V. Phillips, Emre Kıcıman +3
The validity of medical studies based on real-world clinical data, such as observational studies, depends on critical assumptions necessary for drawing causal conclusions about med…
Self-Calibrating Conformal Prediction
Lars van der Laan, Ahmed M. Alaa
In machine learning, model calibration and predictive inference are essential for producing reliable predictions and quantifying uncertainty to support decision-making. Recognizing…
Estimating Uncertainty in Multimodal Foundation Models using Public Internet Data
Shiladitya Dutta, Hongbo Wei, Lars van der Laan +1
Foundation models are trained on vast amounts of data at scale using self-supervised learning, enabling adaptation to a wide range of downstream tasks. At test time, these models e…
Conformal Meta-learners for Predictive Inference of Individual Treatment Effects
Ahmed Alaa, Zaid Ahmad, Mark van der Laan
We investigate the problem of machine learning-based (ML) predictive inference on individual treatment effects (ITEs). Previous work has focused primarily on developing ML-based me…