Causal Identification under Interference: The Role of Treatment Assignment Independence
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910162918309888 |
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| author | Owusu, Julius Márquez, Monika Avila |
| author_facet | Owusu, Julius Márquez, Monika Avila |
| contents | Empirical researchers routinely invoke the no-interference or \textit{individualistic treatment response} (ITR) assumption to identify causal effects in observational studies, despite concerns that interference across units may arise in many economic settings. This paper studies the causal content of standard ITR-based identification formulas when arbitrary interference is present. We show that, under restrictions on dependence between treatment assignments across units, conventional ITR-based identification formulas -- including those underlying selection-on-observables, instrumental variables, regression discontinuity designs, and difference-in-differences -- identify well-defined causal objects: types of \textit{average direct effects} (ADEs). These results do not require knowledge of the interference structure or specification of exposure mappings. We also propose a sensitivity analysis framework that quantifies the robustness of statistical inference to violations of treatment-assignment independence under arbitrary interference. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_22532 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Causal Identification under Interference: The Role of Treatment Assignment Independence Owusu, Julius Márquez, Monika Avila Econometrics Empirical researchers routinely invoke the no-interference or \textit{individualistic treatment response} (ITR) assumption to identify causal effects in observational studies, despite concerns that interference across units may arise in many economic settings. This paper studies the causal content of standard ITR-based identification formulas when arbitrary interference is present. We show that, under restrictions on dependence between treatment assignments across units, conventional ITR-based identification formulas -- including those underlying selection-on-observables, instrumental variables, regression discontinuity designs, and difference-in-differences -- identify well-defined causal objects: types of \textit{average direct effects} (ADEs). These results do not require knowledge of the interference structure or specification of exposure mappings. We also propose a sensitivity analysis framework that quantifies the robustness of statistical inference to violations of treatment-assignment independence under arbitrary interference. |
| title | Causal Identification under Interference: The Role of Treatment Assignment Independence |
| topic | Econometrics |
| url | https://arxiv.org/abs/2604.22532 |