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Causal Interpretation of Estimands Defined by Exposure Mappings


  • Michael P. Leung


In settings with interference, researchers commonly define estimands using exposure mappings to summarize neighborhood variation in treatment assignments. This paper studies the causal interpretation of these estimands under weak restrictions on interference. We demonstrate that the estimands can exhibit unpalatable sign reversals under conventional identification conditions. This motivates the formulation of sign preservation criteria for causal interpretability. To satisfy preferred criteria, it is necessary to impose restrictions on interference, either in potential outcomes or selection into treatment. We provide sufficient conditions and show that they can be satisfied by nonparametric models with interference in both the outcome and selection stages.

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  • Michael P. Leung, 2024. "Causal Interpretation of Estimands Defined by Exposure Mappings," Papers 2403.08183,, revised Mar 2024.
  • Handle: RePEc:arx:papers:2403.08183

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    References listed on IDEAS

    1. Charles F. Manski, 2013. "Identification of treatment response with social interactions," Econometrics Journal, Royal Economic Society, vol. 16(1), pages 1-23, February.
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    7. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
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    9. Edward Vytlacil, 2002. "Independence, Monotonicity, and Latent Index Models: An Equivalence Result," Econometrica, Econometric Society, vol. 70(1), pages 331-341, January.
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