Introduces covariate-adjustment with sample-splitting for finite-sample valid inference on points of the treatment effect distribution, applied to five microcredit RCTs revealing heterogeneous effects despite null averages.
The use of covari- ate adjustment in randomized controlled trials: An overview,
2 Pith papers cite this work. Polarity classification is still indexing.
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Non-collapsible marginal effect measures depend on joint distributions of effect modifiers and prognostic variables, so unadjusted anchored indirect comparisons can be biased even without individual-level treatment effect heterogeneity.
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Predicting the Distribution of Treatment Effects: A Covariate-Adjustment Approach
Introduces covariate-adjustment with sample-splitting for finite-sample valid inference on points of the treatment effect distribution, applied to five microcredit RCTs revealing heterogeneous effects despite null averages.
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Transportability of model-based estimands in evidence synthesis
Non-collapsible marginal effect measures depend on joint distributions of effect modifiers and prognostic variables, so unadjusted anchored indirect comparisons can be biased even without individual-level treatment effect heterogeneity.