Inference for Batched Adaptive Experiments

Abstract:The advantages of adaptive experiments have led to their rapid adoption in economics, other fields, and among practitioners. However, adaptive experiments pose challenges for causal inference. This note suggests a BOLS (batched ordinary least squares) test statistic for inference on treatment effects in adaptive experiments. The statistic provides a precision-equalizing aggregation of per-period treatment-control differences under heteroskedasticity. The combined test statistic is a linear combination of heteroskedastic per-period z-statistics and can be used to construct asymptotically valid confidence intervals. We provide simulation results comparing rejection rates in the typical case with few treatment periods and few (or many) observations per batch.

Econometrics (econ.EM); Machine Learning (cs.LG); Methodology (stat.ME)

arXiv:2512.10156 [econ.EM]

(or arXiv:2512.10156v2 [econ.EM] for this version)

https://doi.org/10.48550/arXiv.2512.10156 Focus to learn more arXiv-issued DOI via DataCite

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