SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

Last update: Nov 28, 2022

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Overview

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

Authors: Alicia Curth*, Changhee Lee*, and Mihaela van der Schaar (* co-first authors)
Paper will be publsihed at NeurIPS 2021.

Owner

Changhee Lee, Ph.D. Candidate at UCLA

GitHub Repository

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SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

Related tags

Overview

SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data

Owner

Procedural 3D data generation pipeline for architecture

PyTorch implementation for the paper Pseudo Numerical Methods for Diffusion Models on Manifolds

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Code for ICE-BeeM paper - NeurIPS 2020

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