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On Defense of the Hazard Ratio
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In this short communication, we describe the recent debate on whether the hazard function should be used for causal inference in time-to-event studies and consider three different potential outcomes frameworks (by Rubin, Robins, and Pearl, respectively) as well as use the single-world intervention graph to show mathematically that the hazard function has causal interpretations under all three frameworks. In addition, we argue that the hazard ratio over time can provide a useful interpretation in practical settings.
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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 le...
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