12 citations · 14 across the 6 of their papers we have counts for
6 papers
Optimized variance estimation under interference and complex experimental designs
Christopher Harshaw, Joel A. Middleton, Fredrik Sävje
Unbiased and consistent variance estimators generally do not exist for design-based treatment effect estimators because experimenters never observe more than one potential outcome…
Exact Bias Correction for Linear Adjustment of Randomized Controlled Trials
Haoge Chang, Joel Middleton, P. M. Aronow
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…
Unifying Design-based Inference: A New Variance Estimation Principle
Joel A. Middleton
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…
Unifying Design-based Inference: On Bounding and Estimating the Variance of any Linear Estimator in any Experimental Design
Joel A. Middleton
This paper provides a design-based framework for variance (bound) estimation in experimental analysis. Results are applicable to virtually any combination of experimental design, l…
How to Account for Alternatives When Comparing Effects: Revisiting 'Bringing Education to Afghan Girls'
Dana Burde, Joel Middleton, Cyrus Samii +1
This paper uses a "principal strata" approach to decompose treatment effects and interpret why a schooling intervention that yielded exceptional initial effects yielded substantial…
A Unified Theory of Regression Adjustment for Design-based Inference
Joel A. Middleton
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 rand…