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March 2016 Full matching approach to instrumental variables estimation with application to the effect of malaria on stunting
Hyunseung Kang, Benno Kreuels, Jürgen May, Dylan S. Small
Ann. Appl. Stat. 10(1): 335-364 (March 2016). DOI: 10.1214/15-AOAS894


Most previous studies of the causal relationship between malaria and stunting have been studies where potential confounders are controlled via regression-based methods, but these studies may have been biased by unobserved confounders. Instrumental variables (IV) regression offers a way to control for unmeasured confounders where, in our case, the sickle cell trait can be used as an instrument. However, for the instrument to be valid, it may still be important to account for measured confounders. The most commonly used instrumental variable regression method, two-stage least squares, relies on parametric assumptions on the effects of measured confounders to account for them. Additionally, two-stage least squares lacks transparency with respect to covariate balance and weighing of subjects and does not blind the researcher to the outcome data. To address these drawbacks, we propose an alternative method for IV estimation based on full matching. We evaluate our new procedure on simulated data and real data concerning the causal effect of malaria on stunting among children. We estimate that the risk of stunting among children with the sickle cell trait decreases by 0.22 per every malaria episode prevented by the sickle cell trait, a substantial effect of malaria on stunting ($p$-value: 0.011, 95% CI: 0.044, 1).


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Hyunseung Kang. Benno Kreuels. Jürgen May. Dylan S. Small. "Full matching approach to instrumental variables estimation with application to the effect of malaria on stunting." Ann. Appl. Stat. 10 (1) 335 - 364, March 2016.


Received: 1 June 2015; Revised: 1 August 2015; Published: March 2016
First available in Project Euclid: 25 March 2016

zbMATH: 1358.62096
MathSciNet: MR3480499
Digital Object Identifier: 10.1214/15-AOAS894

Keywords: Full matching , instrumental variables , malaria , stunting , two-stage least squares

Rights: Copyright © 2016 Institute of Mathematical Statistics

Vol.10 • No. 1 • March 2016
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