Anytime-Valid Inference

We develop anytime-valid inference methods for estimating & testing the time-varying mean score difference between two sequential forecasters. Published in Operations Research.

We develop a statistical inference approach for counterfactually evaluating and comparing abstaining classifiers using nonparametric causal inference. Published in NeurIPS.

We address an intriguing challenge for anytime-valid sequential inference that arises when combining evidence processes constructed using different information sets. Under review.