48 citations · 123 across the 10 of their papers we have counts for
9 papers · 1 filter
Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators
Alicia Curth, Mihaela van der Schaar
The machine learning toolbox for estimation of heterogeneous treatment effects from observational data is expanding rapidly, yet many of its algorithms have been evaluated only on…
A Variational Information Bottleneck Approach to Multi-Omics Data Integration
Changhee Lee, Mihaela van der Schaar
Integration of data from multiple omics techniques is becoming increasingly important in biomedical research. Due to non-uniformity and technical limitations in omics platforms, su…
Synthetic Data: Opening the data floodgates to enable faster, more directed development of machine learning methods
James Jordon, Alan Wilson, Mihaela van der Schaar
Many ground-breaking advancements in machine learning can be attributed to the availability of a large volume of rich data. Unfortunately, many large-scale datasets are highly sens…
Learning "What-if" Explanations for Sequential Decision-Making
Ioana Bica, Daniel Jarrett, Alihan Hüyük +1
Building interpretable parameterizations of real-world decision-making on the basis of demonstrated behavior -- i.e. trajectories of observations and actions made by an expert maxi…
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…