The Annals of Applied Statistics

Full matching approach to instrumental variables estimation with application to the effect of malaria on stunting

Hyunseung Kang, Benno Kreuels, Jürgen May, and Dylan S. Small

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Abstract

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).

Article information

Source
Ann. Appl. Stat. Volume 10, Number 1 (2016), 335-364.

Dates
Received: June 2015
Revised: August 2015
First available in Project Euclid: 25 March 2016

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1458909919

Digital Object Identifier
doi:10.1214/15-AOAS894

Mathematical Reviews number (MathSciNet)
MR3480499

Zentralblatt MATH identifier
06586148

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

Citation

Kang, Hyunseung; Kreuels, Benno; May, Jürgen; Small, Dylan S. Full matching approach to instrumental variables estimation with application to the effect of malaria on stunting. Ann. Appl. Stat. 10 (2016), no. 1, 335--364. doi:10.1214/15-AOAS894. https://projecteuclid.org/euclid.aoas/1458909919


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Supplemental materials

  • Supplement to “Full matching approach to instrumental variables estimation with application to the effect of malaria on stunting”. This document contains theoretical details of our matching method along with extended discussions about our estimator and the estimand. We also present a more detailed data analysis and additional simulation studies.