The aim of causal effect estimation is to find the true impact of a treatment or exposure. Observational data is employed in social sciences to estimate causal effect but is susceptible to self-selection and unobserved confounding biases. Covariates included in analysis should strive to address these biases. This research focuses on investigating covariate selection approaches––common cause criterion (CC), Disjunctive Cause Criterion (DCC), Modified Disjunctive Cause Criterion (MDCC), and modified cause criterion (MCC)––in linear regression (LR) and propensity score methods (PSM) causal effect estimation in the presence of unmeasured confounding. Realistic social science scenarios such as––inclusion of proxy variables with varying degrees of strength, misidentification of the unmeasured covariate as a confounder, small sample sizes, and measurement error in proxy covariates—were investigated. For LR and PSM, five causal effect estimation models were built using different covariate selection approaches and compared on three performance metrics––bias, coverage, and empirical SE. Results showed that in the presence of an unmeasured confounder, the causal effect estimate is biased. Study 1 results indicate that MDCC approach resulted in more consistent and efficient causal effect estimates in the presence of unmeasured confounders. Studies 2a and 2b indicate that the MDCC approach is robust to the unobserved variable being a confounder and can be employed even if the unmeasured covariate is not a confounder without adversely impacting the performance measures. Studies 3 and 4 showed including a proxy of the unmeasured confounder, even a weak proxy (r ~ 0.20) or one with measurement error, results in an improvement in the consistency of the causal effect estimate and in the efficiency of the causal effect estimator. As the correlation between the proxy covariate and the unmeasured confounder gets smaller the causal effect estimator becomes less efficient and the causal effect estimate becomes less consistent.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2263 |
| Date | 01 January 2022 |
| Creators | Nair, Uday |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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