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arXiv · 2008.06564

Optimal selection of the number of control units in kNN algorithm to estimate average treatment effects

Andrés Ramírez-Hassan·Raquel Vargas-Correa·Gustavo García·Daniel Londoño
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Abstract

We propose a simple approach to optimally select the number of control units in k nearest neighbors (kNN) algorithm focusing in minimizing the mean squared error for the average treatment effects. Our approach is non-parametric where confidence intervals for the treatment effects were calculated using asymptotic results with bias correction. Simulation exercises show that our approach gets relative small mean squared errors, and a balance between confidence intervals length and type I error. We analyzed the average treatment effects on treated (ATET) of participation in 401(k) plans on accumulated net financial assets confirming significant effects on amount and positive probability of net asset. Our optimal k selection produces significant narrower ATET confidence intervals compared with common practice of using k=1.

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Andrés Ramírez-Hassan, Raquel Vargas-Correa, Gustavo García, Daniel Londoño. 2020-08-14. Optimal selection of the number of control units in kNN algorithm to estimate average treatment effects. https://arxiv.org/abs/2008.06564

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