Statistical methods for causal inference in observational studies.
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Date
2020
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Abstract
Estimating causal effects is essential in the evaluation of a treatment or intervention.
It is particularly straightforward for well-designed experiments. However,
when the treatment assignment is complicated by confounders, as in the case of
observational studies, such inferences regarding the treatment effects, require more
sophisticated adjustments. In this thesis, we investigated different matching techniques
in terms of how well they balance the treatment groups on the covariates, as
well as their efficiency in estimating treatment effects. We considered the various
algorithm variants of these matching techniques, which include propensity score
matching, Mahalanobis distance matching, and coarsened exact matching. Secondly,
we proposed two new strategies for estimating treatment effects, namely, covariatebalancing
rank-based Mahalanobis distance (CBRMD) and an improved version of
CBRMD (iCBRMD).We evaluated their performance via simulations and some reallife
datasets. Thirdly, we investigated a relatively new optimization-based alternative,
known as entropy balancing, which has been used rarely in the applied biomedical
literature. We shared our experiences learned from using entropy balancing in
non-experimental studies, via Monte Carlo simulations and an empirical application.
We further extended the evaluation of entropy balancing to some standard
measures of causal treatment effects, namely; difference in means, odds ratios, rate
ratios and hazard ratios. We pulled together our evaluations by conducting Monte
Carlo simulations, evaluating both well-established methods and the more recently
proposed methods. These adjustment techniques were evaluated under different
scenarios that align with the practical reality. Finally, we utilized a dataset from a
recently conducted HIV Incidence Provincial Surveillance System (HIPSS) study, to
apply the considered techniques to a public health issue in South Africa.
Description
Doctoral Degree. University of KwaZulu-Natal, Durban.