Joel A. Middleton
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View article: Optimized variance estimation under interference and complex experimental designs
Optimized variance estimation under interference and complex experimental designs Open
Unbiased and consistent variance estimators generally do not exist for design-based treatment effect estimators because experimenters never observe more than one potential outcome for any unit. The problem is exacerbated by interference an…
View article: Exact Bias Correction for Linear Adjustment of Randomized Controlled Trials
Exact Bias Correction for Linear Adjustment of Randomized Controlled Trials Open
In an influential critique of empirical practice, Freedman (2008) showed that the linear regression estimator was biased for the analysis of randomized controlled trials under the randomization model. Under Freedman's assumptions, we deriv…
View article: Unifying Design-based Inference: On Bounding and Estimating the Variance of any Linear Estimator in any Experimental Design
Unifying Design-based Inference: On Bounding and Estimating the Variance of any Linear Estimator in any Experimental Design Open
This paper provides a design-based framework for variance (bound) estimation in experimental analysis. Results are applicable to virtually any combination of experimental design, linear estimator (e.g., difference-in-means, OLS, WLS) and v…
View article: Unifying Design-based Inference: A New Variance Estimation Principle
Unifying Design-based Inference: A New Variance Estimation Principle Open
This paper presents two novel classes of variance estimators with superior properties, in the absence of parametric or semi-parametricassumptions. The first new class of estimator is the Oblozene Chlebizky (OC) variance estimators as a nov…
View article: Unifying Design-based Inference: On Bounding and Estimating the Variance\n of any Linear Estimator in any Experimental Design
Unifying Design-based Inference: On Bounding and Estimating the Variance\n of any Linear Estimator in any Experimental Design Open
This paper provides a design-based framework for variance (bound) estimation\nin experimental analysis. Results are applicable to virtually any combination\nof experimental design, linear estimator (e.g., difference-in-means, OLS, WLS)\nan…
View article: How to Account for Alternatives When Comparing Effects: Revisiting 'Bringing Education to Afghan Girls'
How to Account for Alternatives When Comparing Effects: Revisiting 'Bringing Education to Afghan Girls' Open
This paper uses a "principal strata" approach to decompose treatment effects and interpret why a schooling intervention that yielded exceptional initial effects yielded substantially smaller effects in a replication years later. The specif…
View article: A Unified Theory of Regression Adjustment for Design-based Inference
A Unified Theory of Regression Adjustment for Design-based Inference Open
Under the Neyman causal model, it is well-known that OLS with treatment-by-covariate interactions cannot harm asymptotic precision of estimated treatment effects in completely randomized experiments. But do such guarantees extend to experi…
View article: Potential for Bias Inflation with Grouped Data: A Comparison of Estimators and a Sensitivity Analysis Strategy
Potential for Bias Inflation with Grouped Data: A Comparison of Estimators and a Sensitivity Analysis Strategy Open
We are concerned with the unbiased estimation of a treatment effect in the context of non-experimental studies with grouped or multilevel data.When analyzing such data with this goal, practitioners typically include as many predictors (con…
View article: Replication Data for: Bias Amplification and Bias Unmasking
Replication Data for: Bias Amplification and Bias Unmasking Open
In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates…
View article: Bias Amplification and Bias Unmasking
Bias Amplification and Bias Unmasking Open
In the analysis of causal effects in non-experimental studies, conditioning on observable covariates is one way to try to reduce unobserved confounder bias. However, a developing literature has shown that conditioning on certain covariates…