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Clustering and External Validity in Randomized Controlled Trials

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arxiv 1912.01052 v7 pith:PA6LDBVV submitted 2019-12-02 econ.EM

Antoine Deeb , Clément de Chaisemartin This is my paper · ORCID
classification econ.EM
keywords shockscluster-levelcontrollederrorsindividual-levelinferencerandomizedstandard
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The randomization inference literature studying randomized controlled trials (RCTs) assumes that units' potential outcomes are deterministic. This assumption is unlikely to hold, as stochastic shocks may take place during the experiment. In this paper, we consider the case of an RCT with individual-level treatment assignment, and we allow for individual-level and cluster-level (e.g. village-level) shocks. We show that one can draw inference on the ATE conditional on the realizations of the cluster-level shocks, using heteroskedasticity-robust standard errors, or on the ATE netted out of those shocks, using cluster-robust standard errors.

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  1. Evidence aggregation with ignorance in mind: learning what we do (not) know for archetypes discovery

    econ.EM 2025-01 conditional novelty 7.0 of 10

    A framework jointly learns treatment-effect archetypes and a basin of ignorance, abstaining where generalization is unsupported, with regret and inference guarantees.

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