A General Design-Based Framework and Estimator for Randomized Experiments
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866909736072380416 |
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| author | Harshaw, Christopher Sävje, Fredrik Wang, Yitan |
| author_facet | Harshaw, Christopher Sävje, Fredrik Wang, Yitan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_08698 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A General Design-Based Framework and Estimator for Randomized Experiments Harshaw, Christopher Sävje, Fredrik Wang, Yitan Methodology Econometrics Statistics Theory 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. |
| title | A General Design-Based Framework and Estimator for Randomized Experiments |
| topic | Methodology Econometrics Statistics Theory |
| url | https://arxiv.org/abs/2210.08698 |