Causal Identification under Interference: The Role of Treatment Assignment Independence
Julius Owusu, Monika Avila Márquez
TLDR
This paper shows that standard causal identification methods can still identify average direct effects even with interference, given treatment assignment independence.
Key contributions
- Shows standard causal methods (e.g., IV, DiD) identify average direct effects despite arbitrary interference.
- Identification holds under treatment assignment independence, without needing interference structure knowledge.
- Applies to selection-on-observables, instrumental variables, regression discontinuity, and difference-in-differences.
- Introduces a sensitivity analysis framework for violations of treatment-assignment independence.
Why it matters
This paper validates the use of common causal inference methods, like IV and DiD, in settings with interference, provided treatment assignment independence holds. It clarifies what these methods identify and offers a crucial sensitivity analysis to assess robustness.
Original Abstract
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.
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