48 citations · 131 across the 18 of their papers we have counts for
4 papers · 1 filter
Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning
Changhee Lee, Nicholas Mastronarde, Mihaela van der Schaar
Estimating the individual treatment effect (ITE) from observational data is essential in medicine. A central challenge in estimating the ITE is handling confounders, which are fact…
What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems
Owen Lahav, Nicholas Mastronarde, Mihaela van der Schaar
Recent efforts in Machine Learning (ML) interpretability have focused on creating methods for explaining black-box ML models. However, these methods rely on the assumption that sim…
Forecasting Individualized Disease Trajectories using Interpretable Deep Learning
Ahmed M. Alaa, Mihaela van der Schaar
Disease progression models are instrumental in predicting individual-level health trajectories and understanding disease dynamics. Existing models are capable of providing either a…
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…