Develops novel bounds on average treatment effects by pooling limited information across observations for robustness under unconfoundedness, with inference methods.
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Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.
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Bounding Treatment Effects by Pooling Limited Information across Observations
Develops novel bounds on average treatment effects by pooling limited information across observations for robustness under unconfoundedness, with inference methods.
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Sample size and power calculations for causal inference with time-to-event outcomes
Derives new analytical sample size and power formulas for marginal hazard ratios in causal inference with time-to-event outcomes, applicable to randomized trials and observational studies via IPW estimators.