most citedUnifying Design-based Inference: On Bounding and Estimating the Variance of any Linear Estimator in any Experimental Design

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

stat.ME2021

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…

stat.ME2021

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…

stat.ME20211 cited

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…

stat.AP2021

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…

math.ST2018

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…