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Model-Assisted Analyses of Cluster-Randomized Experiments
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Regression adjustment in completely randomized experiments with a diverging number of covariates
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Regression-assisted inference for the average treatment effect in paired experiments
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Causal Inference from 2K Factorial Designs by Using Potential Outcomes
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On Mitigating the Analytical Limitations of Finely Stratified Experiments
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Encouraging sanitation investment in the developing world: A cluster-randomized trial
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Effectiveness of paramedic practitioners in attending 999 calls from elderly people in the community: cluster randomised controlled trial
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Randomized Experiments in Education, with Implications for Multilevel Causal Inference
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The Essential Role of Pair Matching in Cluster-Randomized Experiments, with Application to the Mexican Universal Health Insurance Evaluation
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Agnostic notes on regression adjustments to experimental data: Reexamining Freedman’s critique
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Rank tests in unmatched clustered randomized trials applied to a study of teacher training
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On the Application of Probability Theory to Agricultural Experiments. Essay on Principles. Section 9
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Unbiased Estimation of the Average Treatment Effect in Cluster-Randomized Experiments
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A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
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Estimators for Clustered Education RCTs Using the Neyman Model for Causal Inference
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Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Design-Based Covariate Adjustment for Causal Inference with Interference and Noncompliance
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Leveraging baseline covariates to analyze small cluster‐randomized trials with a rare binary outcome
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Model‐Robust Standardization in Cluster‐Randomized Trials
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Multilevel Intervention Stepped Wedge Designs (MLI-SWDs)
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Two weights make a wrong: Cluster randomized trials with variable cluster sizes and heterogeneous treatment effects
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Covariate-adjusted Fisher randomization tests for the average treatment effect
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Model-Robust and Efficient Covariate Adjustment for Cluster-Randomized Experiments
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Model-Assisted Complier Average Treatment Effect Estimates in Randomized Experiments with Noncompliance
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Clarifying Selection Bias in Cluster Randomized Trials: Estimands and Estimation
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