@TechReport{iza:izadps:dp18595,
            	author={Bia, Michela Gianna and Menta, Giorgia and Huber, Martin and D'Ambrosio, Conchita},
            	title={Harnessing Genetic Variants for Local Average Treatment Effect Estimation},
            	year={2026},
	month={Apr},
            	institution={Institute of Labor Economics (IZA)},
            	address={Bonn},
            	type={IZA Discussion Paper},
            	number={18595},
            	url={https://www.iza.org/publications/dp18595},
	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},
            }