Partial identification under stratified randomization

This paper develops a unified framework for partial identification and inference in stratified experiments with attrition, accommodating both equal and heterogeneous treatment shares across strata. For equal-share designs, we apply recent theory for finely stratified experiments to standard Lee bounds, yielding closed-form, design-consistent variance estimators and valid inference. Simulations show that the conventional formula can overstate uncertainty, while our approach delivers tighter confidence sets. When treatment shares differ across strata, we propose a new strategy, which combines inverse probability weighting and global trimming to construct valid bounds even when strata are small or unbalanced. We establish identification, introduce a moment estimator, and extend existing inference results to stratified designs with heterogeneous shares, covering a broad class of moment-based estimators that includes the one we formulate. We also generalize our results to designs in which strata are defined solely by observed labels.