Causal Inference for Aggregated Treatment

Carolina Caetano, Gregorio Caetano, Brantly Callaway and Derek Dyal

Papers from arXiv.org

Abstract: In this paper, we study causal inference when the treatment variable is an aggregation of multiple sub-treatment variables. Researchers often report marginal causal effects for the aggregated treatment, implicitly assuming that the target parameter corresponds to a well-defined average of sub-treatment effects. We show that, even in an ideal scenario for causal inference such as random assignment, the weights underlying this average have some key undesirable properties: they are not unique, they can be negative, and, holding all else constant, these issues become exponentially more likely to occur as the number of sub-treatments increases and the support of each sub-treatment grows. We propose approaches to avoid these problems, depending on whether or not the sub-treatment variables are observed.

Date: 2025-06
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2506.22885

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