Search Results for author:
Found 15 papers, 7 papers with code
Multitask Boosting for Survival Analysis with Competing Risks
The co-occurrence of multiple diseases among the general population is an important problem as those patients have more risk of complications and represent a large share of health care expenditure.
Kernel Hypothesis Testing with Set-valued Data
We present a general framework for hypothesis testing on distributions of sets of individual examples.
Conditional Independence Testing using Generative Adversarial Networks
We consider the hypothesis testing problem of detecting conditional dependence, with a focus on high-dimensional feature spaces.
A Bayesian Approach to Modelling Longitudinal Data in Electronic Health Records
Analyzing electronic health records (EHR) poses significant challenges because often few samples are available describing a patient's health and, when available, their information content is highly diverse.
Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes
Comorbid diseases co-occur and progress via complex temporal patterns that vary among individuals.
Learning Overlapping Representations for the Estimation of Individualized Treatment Effects
The choice of making an intervention depends on its potential benefit or harm in comparison to alternatives.
Accounting for Unobserved Confounding in Domain Generalization
Part of the challenge of learning robust models lies in the influence of unobserved confounders that void many of the invariances and principles of minimum error presently used for this problem.
Policy Analysis using Synthetic Controls in Continuous-Time
Counterfactual estimation using synthetic controls is one of the most successful recent methodological developments in causal inference.
Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding
Unobserved confounding is one of the greatest challenges for causal discovery.
Neural graphical modelling in continuous-time: consistency guarantees and algorithms
In this paper, we consider score-based structure learning for the study of dynamical systems.
MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms
Missing data is an important problem in machine learning practice.
Generalization bounds and algorithms for estimating conditional average treatment effect of dosage
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 relationships in the underlying system.
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
To assess solutions to this problem, we propose a controllable simulation environment based on a model of tumor growth for a range of scenarios with irregular sampling reflective of a variety of clinical scenarios.
Functional Causal Bayesian Optimization
We propose functional causal Bayesian optimization (fCBO), a method for finding interventions that optimize a target variable in a known causal graph.
Towards Bounding Causal Effects under Markov Equivalence
In this more "data-driven" setting, we provide a systematic algorithm to derive bounds on causal effects that can be computed analytically.
