Deep cox mixtures for survival regression
Survival analysis is a challenging variation of regression modeling because of the presence
of censoring, where the outcome measurement is only partially known, due to, for example …
of censoring, where the outcome measurement is only partially known, due to, for example …
Causal rule ensemble: Interpretable discovery and inference of heterogeneous treatment effects
FJ Bargagli-Stoffi, R Cadei, K Lee… - ar** with censored time-to-events
Estimation of treatment efficacy of real-world clinical interventions involves working with
continuous time-to-event outcomes such as time-to-death, re-hospitalization, or a composite …
continuous time-to-event outcomes such as time-to-death, re-hospitalization, or a composite …
Fair and robust estimation of heterogeneous treatment effects for policy learning
K Kim, JR Zubizarreta - International Conference on …, 2023 - proceedings.mlr.press
We propose a simple and general framework for nonparametric estimation of
heterogeneous treatment effects under fairness constraints. Under standard regularity …
heterogeneous treatment effects under fairness constraints. Under standard regularity …
Computer-aided diagnosis through medical image retrieval in radiology
Currently, radiologists face an excessive workload, which leads to high levels of fatigue, and
consequently, to undesired diagnosis mistakes. Decision support systems can be used to …
consequently, to undesired diagnosis mistakes. Decision support systems can be used to …
Auton-survival: an open-source package for regression, counterfactual estimation, evaluation and phenoty** with censored time-to-event data
Applications of machine learning in healthcare often require working with time-to-event
prediction tasks including prognostication of an adverse event, re-hospitalization, and …
prediction tasks including prognostication of an adverse event, re-hospitalization, and …
Probing Digital Footprints and Reaching for Inherent Preferences: A Cause-Disentanglement Approach to Personalized Recommendations
The abundance of multiple types of consumer digital footprints recorded on e-commerce
platforms has fueled the design of personalized recommender systems for decision support …
platforms has fueled the design of personalized recommender systems for decision support …
Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines
The dearth of prescribing guidelines for physicians is one key driver of the current opioid
epidemic in the United States. In this work, we analyze medical and pharmaceutical claims …
epidemic in the United States. In this work, we analyze medical and pharmaceutical claims …
On the Intersection of Explainable and Reliable AI for physical fatigue prediction
In the era of Industry 4.0, the use of Artificial Intelligence (AI) is widespread in occupational
settings. Since dealing with human safety, explainability and trustworthiness of AI are even …
settings. Since dealing with human safety, explainability and trustworthiness of AI are even …
CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect
In causal inference, estimating heterogeneous treatment effects (HTE) is critical for
identifying how different subgroups respond to interventions, with broad applications in …
identifying how different subgroups respond to interventions, with broad applications in …