5 papers · 1 filter
Transporting treatment effects by calibrating large-scale observational outcomes
Harrison H Li
A high-quality experimental dataset is often much smaller than a corresponding observational dataset. When this holds with possibly biased measurements of the outcome of interest i…
Setting the duration of online A/B experiments
Harrison H. Li, Chaoyu Yu
In designing an online A/B experiment, it is crucial to select a sample size and duration that ensure the resulting confidence interval (CI) for the treatment effect is the right w…
Efficient estimation and data fusion under general semiparametric restrictions on outcome mean functions
Harrison H. Li
We provide a novel characterization of semiparametric efficiency in a generic supervised learning setting where the outcome mean function -- defined as the conditional expectation…
Double machine learning and design in batch adaptive experiments
Harrison H. Li, Art B. Owen
We consider an experiment with at least two stages or batches and subjects per batch. First, we propose a semiparametric treatment effect estimator that efficiently pools in…
A general characterization of optimal tie-breaker designs
Harrison H. Li, Art B. Owen
Tie-breaker designs trade off a statistical design objective with short-term gain from preferentially assigning a binary treatment to those with high values of a running variable $…