%0 Report
        %A Bia, Michela Gianna
%A Menta, Giorgia
%A Huber, Martin
%A D'Ambrosio, Conchita
        %T Harnessing Genetic Variants for Local Average Treatment Effect Estimation
        %D 2026
        %8 2026 Apr
        %I Institute of Labor Economics (IZA)
        %C Bonn
        %7 IZA Discussion Paper
        %N 18595
        %U https://www.iza.org/publications/dp18595
        %X 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.
        %K causal inference
%K LATE
%K heterogeneous treatments
%K instrumental variables
%K Mendelian Randomization