Ego-cluster randomization with model-based estimators enables consistent and asymptotically normal estimation of global treatment effects and spillovers in networks with interference.
Handling limited overlap in observational studies with cardinality matching
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Develops covariate-adjusted estimators for treatment effects under interference that achieve asymptotic unbiasedness and a no-harm variance guarantee relative to the unadjusted estimator.
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.
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Estimating Treatment and Spillover Effects with the Ego-Cluster Experimental Design
Ego-cluster randomization with model-based estimators enables consistent and asymptotically normal estimation of global treatment effects and spillovers in networks with interference.
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Covariate Adjustment Cannot Hurt: Treatment Effect Estimation under Interference with Low-Order Outcome Interactions
Develops covariate-adjusted estimators for treatment effects under interference that achieve asymptotic unbiasedness and a no-harm variance guarantee relative to the unadjusted estimator.
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A Review of Causal Decision Making
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.