Eric Auerbach, Jonathan Auerbach, Max Tabord-Meehan
arXiv 11 Jan 2024 · Econometrics
arXiv:2401.06264 · PDF · DOI · OpenAlex · Extracted main text
We thank Savje (2023) for a thought-provoking article and appreciate the opportunity to share our perspective as social scientists. In his article, Savje recommends misspecified exposure effects as a way to avoid strong assumptions about interference when analyzing the results of an experiment. In this invited discussion, we highlight a limiation of Savje's recommendation: exposure effects are not generally useful for evaluating social policies without the strong assumptions that Savje seeks to avoid.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Aronow, P.M. & Samii, C (2017) Estimating average causal effects under general interference | 0.644 | 2 | 2 | 100% |
| 2 | Auerbach, E., & Tabord-Meehan, M (2021) The local approach to causal inference under network interference self | 0.405 | 1 | 1 | 100% |
| 3 | Manski, C. F (2013) Identification of treatment response with social interactions | 0.405 | 1 | 1 | 100% |
| 4 | Sävje, F (2023) Causal inference with misspecified exposure mappings: separating definitions and assumptions | 0.405 | 1 | 1 | 100% |
Showing the top 4 of 4 scored citations.