About: Survival Analysis meets Counterfactual Inference

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Survival Analysis meets Counterfactual Inference
Creator	Henao, Ricardo Carin, Lawrence Assaad, Serge Chapfuwa, Paidamoyo Pencina, Michael Zeng, Shuxi
Source	ArXiv
abstract	There is growing interest in applying machine learning methods for counterfactual inference from observational data. However, approaches that account for survival outcomes are relatively limited. Survival data are frequently encountered across diverse medical applications, /textit{i.e.}, drug development, risk profiling, and clinical trials, and such data are also relevant in fields like manufacturing (for equipment monitoring). When the outcome of interest is time-to-event, special precautions for handling censored events need to be taken, as ignoring censored outcomes may lead to biased estimates. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes. Further, we formulate a nonparametric hazard ratio metric for evaluating average and individualized treatment effects. Experimental results on real-world and semi-synthetic datasets, the latter which we introduce, demonstrate that the proposed approach significantly outperforms competitive alternatives in both survival-outcome predictions and treatment-effect estimation.
has issue date	2020-06-14(xsd:dateTime)
has license	arxiv
sha1sum (hex)	e2da56ab928ec91729afb5558976335b5c33f997
resource representing a document's title	Survival Analysis meets Counterfactual Inference
resource representing a document's body	covid:e2da56ab928ec91729afb5558976335b5c33f997#body_text
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