Inference for Batched Adaptive Experiments

Jan Kemper and Davud Rostam-Afschar

No 1854, GLO Discussion Paper Series from Global Labor Organization (GLO)

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 precisionequalizing 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.

Keywords: Adaptive experiments; Heteroskedasticity; Causal inference; Randomized controlled trial (search for similar items in EconPapers)
JEL-codes: C12 C13 C9 D83 (search for similar items in EconPapers)
Date: 2026
New Economics Papers: this item is included in nep-exp
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Related works:
Working Paper: Inference for Batched Adaptive Experiments (2026)
Working Paper: Inference for batched adaptive experiments (2025)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:glodps:1854

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