Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes

Key points

• Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding.1
• Proximal causal inference addresses unmeasured confounding through proxy variables, but existing proximal survival methods rely largely on restrictive parametric bridge models.1
• Building on existing proximal identification of treatment-specific survival curves, the framework yields outcome-bridge (OB), treatment-bridge (TB), and doubly robust (DR) estimators, with two flexible bridge learners: PSB-K, a kernel estimator with a low-rank time decomposition, and PSB-N, a neural estimator trained by a projected maximum-moment-restriction loss.1
• Unlike existing flexible proximal learners for scalar outcomes, PSB directly handles censoring and survival-curve targets.1

Sources (1)

• [1]Flexible Doubly Robust Proximal Causal Inference for Censored Outcomes
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 08:21 PM

  We introduce Proximal Survival Bridge learning (PSB), a flexible doubly robust proximal framework for censored outcomes.

  Estimating causal effects on right-censored time-to-event outcomes is challenging under unmeasured confounding.

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