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Causal Inference : controlling for bias in observational studies using propensity score methods
Dissertation (MSc)--University of Pretoria, 2020.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2020
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| _version_ | 1867613717588344832 |
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| access_status_str | Open Access |
| author2 | Fletcher, Lizelle |
| author_browse | Fletcher, Lizelle |
| author_facet | Fletcher, Lizelle |
| collection | Thesis |
| dc_rights_str_mv | © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2020. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/77378 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:35.305Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/77378 Causal Inference : controlling for bias in observational studies using propensity score methods Fletcher, Lizelle mxo.msibi@gmail.com Msibi, Mxolisi Mathematical Statistics conditional independence propensity score counterfactual UCTD Dissertation (MSc)--University of Pretoria, 2020. Adjusting for baseline pre-intervention characteristics between treatment groups, through the use of propensity score matching methods, is an important step that enables researchers to do causal inference with confidence. This is critical, largely, due to the fact that practical treatment allocation scenarios are non-randomized in nature, with various inherent biases that are inevitable, and therefore requiring such experimental manipulations. These propensity score matching methods are the available tools to be used as control mechanisms, for such intrinsic system biases in causal studies, without the benefits of randomization (Lane, To, Kyna , & Robin, 2012). Certain assumptions need to be verifiable or met, before one may embark on a propensity score matching causal effects journey, using the Rubin causal model (Holland, 1986), of which the main ones are conditional independence (unconfoundedness) and common support (positivity). In particular, with this dissertation we are concerned with elaborating the applications of these matching methods, for a ‘strong-ignorability’ case (Rosenbaum & Rubin, 1983), i.e. when both the overlap and unconfoundedness properties are valid. We will take a journey from explaining different experimental designs and how the treatment effect is estimated, closing with a practical example based on two cohorts of enrolled introductory statistics students prior and post-clickers intervention, at a public South African university, and the relevant causal conclusions thereof. Keywords: treatment, conditional independence, propensity score, counterfactual, confounder, common support Statistics MSc Unrestricted 2020-12-15T09:58:56Z 2020-12-15T09:58:56Z 2021 2020 Dissertation * A2021 http://hdl.handle.net/2263/77378 en © 2019 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Mathematical Statistics conditional independence propensity score counterfactual UCTD Causal Inference : controlling for bias in observational studies using propensity score methods |
| title | Causal Inference : controlling for bias in observational studies using propensity score methods |
| title_full | Causal Inference : controlling for bias in observational studies using propensity score methods |
| title_fullStr | Causal Inference : controlling for bias in observational studies using propensity score methods |
| title_full_unstemmed | Causal Inference : controlling for bias in observational studies using propensity score methods |
| title_short | Causal Inference : controlling for bias in observational studies using propensity score methods |
| title_sort | causal inference controlling for bias in observational studies using propensity score methods |
| topic | Mathematical Statistics conditional independence propensity score counterfactual UCTD |
| url | http://hdl.handle.net/2263/77378 |