599 citations · 1.6k across the 142 of their papers we have counts for
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Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time
Jeroen Berrevoets, Alicia Curth, Ioana Bica +2
Choosing the best treatment-plan for each individual patient requires accurate forecasts of their outcome trajectories as a function of the treatment, over time. While large observ…
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Trent Kyono, Yao Zhang, Alexis Bellot +1
Missing data is an important problem in machine learning practice. Starting from the premise that imputation methods should preserve the causal structure of the data, we develop a…
DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks
Boris van Breugel, Trent Kyono, Jeroen Berrevoets +1
Machine learning models have been criticized for reflecting unfair biases in the training data. Instead of solving for this by introducing fair learning algorithms directly, we foc…
Explaining Latent Representations with a Corpus of Examples
Jonathan Crabbé, Zhaozhi Qian, Fergus Imrie +1
Modern machine learning models are complicated. Most of them rely on convoluted latent representations of their input to issue a prediction. To achieve greater transparency than a…
Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression
Zhaozhi Qian, William R. Zame, Lucas M. Fleuren +2
Modeling a system's temporal behaviour in reaction to external stimuli is a fundamental problem in many areas. Pure Machine Learning (ML) approaches often fail in the small sample…
Explaining Time Series Predictions with Dynamic Masks
Jonathan Crabbé, Mihaela van der Schaar
How can we explain the predictions of a machine learning model? When the data is structured as a multivariate time series, this question induces additional difficulties such as the…
The Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation
Alex J. Chan, Ioana Bica, Alihan Huyuk +2
Understanding decision-making in clinical environments is of paramount importance if we are to bring the strengths of machine learning to ultimately improve patient outcomes. Sever…
Scalable Bayesian Inverse Reinforcement Learning
Alex J. Chan, Mihaela van der Schaar
Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem. Unfortunately current methods generally do not…
SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups
Hyun-Suk Lee, Cong Shen, William Zame +2
Phase I clinical trials are designed to test the safety (non-toxicity) of drugs and find the maximum tolerated dose (MTD). This task becomes significantly more challenging when mul…