Safe Sequential Testing and Effect Estimation in Stratified Count Data

@InProceedings{pmlr-v206-turner23a,
  title = 	 {Safe Sequential Testing and Effect Estimation in Stratified Count Data},
  author =       {Turner, Rosanne and Grunwald, Peter},
  booktitle = 	 {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {4880--4893},
  year = 	 {2023},
  editor = 	 {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem},
  volume = 	 {206},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {25--27 Apr},
  publisher =    {PMLR},
  pdf = 	 {https://proceedings.mlr.press/v206/turner23a/turner23a.pdf},
  url = 	 {https://proceedings.mlr.press/v206/turner23a.html},
  abstract = 	 {Sequential decision making significantly speeds up research and is more cost-effective compared to fixed-n methods. We present a method for sequential decision making for stratified count data that retains Type-I error guarantee or false discovery rate under optional stopping, using e-variables. We invert the method to construct stratified anytime-valid confidence sequences, where cross-talk between subpopulations in the data can be allowed during data collection to improve power. Finally, we combine information collected in separate subpopulations through pseudo-Bayesian averaging and switching to create effective estimates for the minimal, mean and maximal treatment effects in the subpopulations.}
}