Causal Inference With Non-probability Sample and Misclassified Covariate
| dc.contributor.advisor | Shen, Hua | |
| dc.contributor.author | Sevinc, Emir | |
| dc.contributor.committeemember | Lu, Xuewen | |
| dc.contributor.committeemember | Deardon, Robert | |
| dc.contributor.committeemember | Shen, Hua | |
| dc.contributor.committeemember | Badescu, Alexandru | |
| dc.date | 2022-11 | |
| dc.date.accessioned | 2022-09-27T16:21:15Z | |
| dc.date.available | 2022-09-27T16:21:15Z | |
| dc.date.issued | 2022-09 | |
| dc.description.abstract | Causal inference refers to the study of analyzing data that is explicitly defined on a question of causality. The problems motivating many, if not most studies in social and biological sciences, tend to be causative and not associative. A well defined and systematically representative sample tends to be the base in such studies. However, sometimes a sample may result from a non-probability process. This often provides a unique challenge in estimating the probability of an individual being in the sample, and generalizing the causality conclusions made off of the non-probability samples to the target population. Additionally, due to issues such as difficulty of precise measurements and human error, certain variables may be classified incorrectly. In this thesis, we address both challenges by implementing causal inferential methods in a case where we have a main non-probability sample with response available, and a probability sample with auxiliary information only. We deal with the presence of incorrectly classified confounder in the non-probability sample only, or both samples. We examine the consequences of naively ignoring misclassification, and develop a latent-variable based method via an Expectation-Maximization algorithm to correct for the misclassified confounder. We incorporate this method with a double-robust mean estimator requiring only the correct specification of either the regression model or the non-probability sample selection model to estimate the average treatment effect. We demonstrate the effectiveness of our methodology via simulation studies, and implement it on smoking data from the Centre of Disease Control and Prevention (CDC). | en_US |
| dc.identifier.citation | Sevinc, E. (2022). Causal inference with non-probability sample and misclassified covariate (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. | en_US |
| dc.identifier.uri | http://hdl.handle.net/1880/115301 | |
| dc.identifier.uri | https://dx.doi.org/10.11575/PRISM/40307 | |
| dc.language.iso | eng | en_US |
| dc.publisher.faculty | Science | en_US |
| dc.publisher.institution | University of Calgary | en |
| dc.rights | University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission. | en_US |
| dc.subject.classification | Statistics | en_US |
| dc.title | Causal Inference With Non-probability Sample and Misclassified Covariate | en_US |
| dc.type | master thesis | en_US |
| thesis.degree.discipline | Mathematics & Statistics | en_US |
| thesis.degree.grantor | University of Calgary | en_US |
| thesis.degree.name | Master of Science (MSc) | en_US |
| ucalgary.item.requestcopy | true | en_US |