13 citations · 28 across the 4 of their papers we have counts for
7 papers
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…
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…
Policy Analysis using Synthetic Controls in Continuous-Time
Alexis Bellot, Mihaela van der Schaar
Counterfactual estimation using synthetic controls is one of the most successful recent methodological developments in causal inference. Despite its popularity, the current descrip…
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Zhaozhi Qian, Ahmed M. Alaa, Alexis Bellot +2
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals. In electronic health records we can observe the different diseases a patient has,…
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…
Kernel Hypothesis Testing with Set-valued Data
Alexis Bellot, Mihaela van der Schaar
We present a general framework for hypothesis testing on distributions of sets of individual examples. Sets may represent many common data sources such as groups of observations in…