Lost in Aggregation: The Causal Interpretation of the IV Estimand
Danielle Tsao, Krikamol Muandet, Frederick Eberhardt, Emilija Perkovi\'c

TL;DR
This paper examines the limitations of instrumental variable methods when estimating effects of aggregate treatments composed of multiple components, highlighting interpretational ambiguities and conditions for valid identification.
Contribution
It introduces the concept of aggregate treatment variables and formalizes how their effects are ambiguous, providing conditions for when IV estimates are valid.
Findings
Instrumental variable estimates can be ambiguous for aggregate treatments.
Standard IV estimators identify effects only under restrictive conditions.
Highlights the need for broader justification of exclusion restrictions in aggregate settings.
Abstract
Instrumental variable based estimation of a causal effect has emerged as a standard approach to mitigate confounding bias in the social sciences and epidemiology, where conducting randomized experiments can be too costly or impossible. However, justifying the validity of the instrument often poses a significant challenge. In this work, we highlight a problem generally neglected in arguments for instrumental variable validity: the presence of an ''aggregate treatment variable'', where the treatment (e.g., education, GDP, caloric intake) is composed of finer-grained components that each may have a different effect on the outcome. We show that the causal effect of an aggregate treatment is generally ambiguous, as it depends on how interventions on the aggregate are instantiated at the component level, formalized through the aggregate-constrained component intervention distribution. We then…
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Taxonomy
TopicsAdvanced Causal Inference Techniques · Intergenerational and Educational Inequality Studies · Health disparities and outcomes
