Doubly Robust Learning of Heterogeneous Survival Treatment Effects under Time-Varying Treatments

Key points

• Estimating heterogeneous survival treatment effects (HSTEs) from time-to-event data under time-varying treatments is important in fields such as medicine (e.g., to decide which sequence of treatments is better in terms of survival probability).1
• However, this task is challenging because treatment decisions, patient characteristics, and censoring probability often evolve together, requiring sequential adjustment for both treatments and censoring.1
• Existing survival learners mostly consider a single baseline treatment assignment, while longitudinal treatment-effect learners typically assume uncensored outcomes.1
• The framework accommodates flexible machine learning models for nuisance estimation and treatment-effect regression.1

Sources (1)

• [1]Doubly Robust Learning of Heterogeneous Survival Treatment Effects under Time-Varying Treatments
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 04:44 PM

  In this paper, we introduce a novel doubly robust learner for heterogeneous survival treatment effects under time-varying treatments and history-dependent censoring.

  Estimating heterogeneous survival treatment effects (HSTEs) from time-to-event data under time-varying treatments is important in fields such as medicine (e.g., to decide which sequence of treatments is better in terms of survival probability).

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