Christopher Harshaw, Fredrik Sävje, Yitan Wang
arXiv 17 Oct 2022 · Statistics — Methodology
arXiv:2210.08698 · PDF · DOI · OpenAlex · Extracted main text
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.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Coupling Designs for Randomized Experiments with Complex Treatments | 0.644 | 2 | 2 |