48 citations · 123 across the 10 of their papers we have counts for
6 papers · 1 filter
Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding
Alexis Bellot, Mihaela van der Schaar
Unobserved confounding is one of the greatest challenges for causal discovery. The case in which unobserved variables have a widespread effect on many of the observed ones is parti…
Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms
Alicia Curth, Mihaela van der Schaar
The need to evaluate treatment effectiveness is ubiquitous in most of empirical science, and interest in flexibly investigating effect heterogeneity is growing rapidly. To do so, a…
Contextual Constrained Learning for Dose-Finding Clinical Trials
Hyun-Suk Lee, Cong Shen, James Jordon +1
Clinical trials in the medical domain are constrained by budgets. The number of patients that can be recruited is therefore limited. When a patient population is heterogeneous, thi…
A Bayesian Approach to Modelling Longitudinal Data in Electronic Health Records
Alexis Bellot, Mihaela van der Schaar
Analyzing electronic health records (EHR) poses significant challenges because often few samples are available describing a patient's health and, when available, their information…
Lifelong Bayesian Optimization
Yao Zhang, James Jordon, Ahmed M. Alaa +1
Automatic Machine Learning (Auto-ML) systems tackle the problem of automating the design of prediction models or pipelines for data science. In this paper, we present Lifelong Baye…
Disease-Atlas: Navigating Disease Trajectories with Deep Learning
Bryan Lim, Mihaela van der Schaar
Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. While there are many advantages to joint…