A debiased maximum-likelihood estimator for hazard ratios in a baseline-hazard-free exponential model with kernel ML adjustment recovers true causal hazard ratios in simulations, but a key convergence assumption is left as a conjecture.
Subtleties in the inter- pretation of hazard contrasts,
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Debiased maximum-likelihood estimators for hazard ratios under kernel-based machine-learning adjustment
A debiased maximum-likelihood estimator for hazard ratios in a baseline-hazard-free exponential model with kernel ML adjustment recovers true causal hazard ratios in simulations, but a key convergence assumption is left as a conjecture.