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A General Design-Based Framework and Estimator for Randomized Experiments
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We describe a design-based framework for drawing causal inference in general randomized experiments. Causal effects are defined as linear functionals evaluated at unit-level potential outcome functions. Assumptions about the potential outcome functions are encoded as function spaces. This makes the framework expressive, allowing experimenters to formulate and investigate a wide range of causal questions, including about interference, that previously could not be investigated with design-based methods. We describe a class of estimators for estimands defined using the framework and investigate their properties. We provide necessary and sufficient conditions for unbiasedness and consistency. We also describe a class of conservative variance estimators, which facilitate the construction of confidence intervals. Finally, we provide several examples of empirical settings that previously could not be examined with design-based methods to illustrate the use of our approach in practice.
Forward citations
Cited by 4 Pith papers
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Coupling Designs for Randomized Experiments with Complex Treatments
Matching units into homogeneous groups and assigning highly dispersed treatments via Monte Carlo couplings improves estimation efficiency in experiments with complex treatment spaces, with gains equal to dispersion ti...
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