Harnessing Genetic Variants for Local Average Treatment Effect Estimation

Michela Bia (), Giorgia Menta (), Martin Huber and Conchita D'Ambrosio
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Michela Bia: LISER and University of Luxembourg
Giorgia Menta: LISER
Martin Huber: University of Fribourg
Conchita D'Ambrosio: University of Luxembourg

No 18595, IZA Discussion Papers from IZA Network @ LISER

Abstract: When multiple instruments are available, conventional instrumental variable estimators aggregate across heterogeneous margins of compliance, often yielding effects without a clear economic interpretation. This issue worsens when some instruments violate the exclusion restriction, as in settings using genetic variants. We propose a clustering-based plurality framework for instrumental variable estimation that addresses both instrument heterogeneity and invalid instruments. Rather than imposing a single causal parameter, our method groups instruments by similarity in the first stage and applies a plurality rule on subgroups with similar reduced-form relationships to identify locally valid subsets. This produces a set of margin-specific local average treatment effects instead of a single pooled estimate. We extend plurality-based identification to settings with non-mutually exclusive instruments, such as Mendelian Randomization designs. We illustrate the method in a two-sample Mendelian Randomization study of the effect of education on smoking. Results confirm a negative causal effect while revealing substantial heterogeneity across instrument-defined margins, masked by pooled IV approaches.

Keywords: causal inference; LATE; heterogeneous treatments; instrumental variables; Mendelian Randomization (search for similar items in EconPapers)
JEL-codes: C31 C36 I10 (search for similar items in EconPapers)
Date: 2026-04
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