4 citations · 9 across the 7 of their papers we have counts for
5 papers · 1 filter
A General Design-Based Framework and Estimator for Randomized Experiments
Christopher Harshaw, Fredrik Sävje, Yitan Wang
We describe a widely applicable design-based framework for drawing causal inference in randomized experiments. Causal effects are defined as linear functionals evaluated at unit-le…
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…
Randomization does not imply unconfoundedness
Fredrik Sävje
A common assumption in causal inference is that random treatment assignment ensures that potential outcomes are independent of treatment, or in one word, unconfoundedness. This pap…
Balancing Covariates in Randomized Experiments with the Gram-Schmidt Walk Design
Christopher Harshaw, Fredrik Sävje, Daniel Spielman +1
The design of experiments involves a compromise between covariate balance and robustness. This paper provides a formalization of this trade-off and describes an experimental design…
The performance and efficiency of Threshold Blocking
Fredrik Sävje
A common method to reduce the uncertainty of causal inferences from experiments is to assign treatments in fixed proportions within groups of similar units: blocking. Previous resu…