26 citations · 76 across the 14 of their papers we have counts for
14 papers
Foundation Models for Partial Causal Identification
Alexis Bellot, Anish Dhir
This paper investigates the development of causal foundation models for bounding the effect of interventions and counterfactuals from observational data. We show that a canonical p…
Towards Bounding Causal Effects under Markov Equivalence
Alexis Bellot
Predicting the effect of unseen interventions is a fundamental research question across the data sciences. It is well established that in general such questions cannot be answered…
Functional Causal Bayesian Optimization
Limor Gultchin, Virginia Aglietti, Alexis Bellot +1
We propose functional causal Bayesian optimization (fCBO), a method for finding interventions that optimize a target variable in a known causal graph. fCBO extends the CBO family o…
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
Nabeel Seedat, Fergus Imrie, Alexis Bellot +2
Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare by assisting decision-makers to answer ''what-iF'' questions. Existing causal infer…
Generalization bounds and algorithms for estimating conditional average treatment effect of dosage
Alexis Bellot, Anish Dhir, Giulia Prando
We investigate the task of estimating the conditional average causal effect of treatment-dosage pairs from a combination of observational data and assumptions on the causal relatio…
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…
Neural graphical modelling in continuous-time: consistency guarantees and algorithms
Alexis Bellot, Kim Branson, Mihaela van der Schaar
The discovery of structure from time series data is a key problem in fields of study working with complex systems. Most identifiability results and learning algorithms assume the u…
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…
Accounting for Unobserved Confounding in Domain Generalization
Alexis Bellot, Mihaela van der Schaar
This paper investigates the problem of learning robust, generalizable prediction models from a combination of multiple datasets and qualitative assumptions about the underlying dat…
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…
Conditional Independence Testing using Generative Adversarial Networks
Alexis Bellot, Mihaela van der Schaar
We consider the hypothesis testing problem of detecting conditional dependence, with a focus on high-dimensional feature spaces. Our contribution is a new test statistic based on s…