  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 decrease by 0.22 times the average number of malaria
episodes prevented by the sickle cell trait, a substantial effect of malaria on
stunting (p-value: 0.011, 95% CI: 0.044, 1).
