Contributions to multivariate matching in observational studies / Ruoqi Yu.

Format:

Book

Thesis/Dissertation

Author/Creator:

Yu, Ruoqi, author.

Contributor:

Rosenbaum, Paul R., degree supervisor.

University of Pennsylvania. Department of Statistics, degree granting institution.

Language:

English

Subjects (All):

Statistics.

Statistics--Penn dissertations.

Penn dissertations--Statistics.

Local Subjects:

Statistics.

Statistics--Penn dissertations.

Penn dissertations--Statistics.

Genre:

Academic theses.

Physical Description:

1 online resource (116 pages)

Contained In:

Dissertations Abstracts International 82-12B.

Place of Publication:

[Philadelphia, Pennsylvania] : University of Pennsylvania ; Ann Arbor : ProQuest Dissertations & Theses, 2021.

Language Note:

English

System Details:

Mode of access: World Wide Web.

text file

Summary:

Matching is a common approach to reduce bias in observed covariates to draw reliable causal inferences in observational studies. This thesis consists of three papers discussing new methods for conducting, evaluating, and improving matching designs in observational studies. The first paper presents new optimal matching techniques for large-scale observational data. This new method reduces the computational complexity and preserves appealing properties in terms of balancing covariates. After constructing a matched sample, it is essential to assess the covariate balance of the matched data since lack of balance in covariates can induce a bias of the estimated treatment effect. The second paper discusses a formal evaluation of covariate balance. This new assessment evaluates whether the match is adequate compared to randomized experiments and identifies the major problems, guiding how to improve the covariate balance. If diagnostics suggest that the current match is not satisfactory, how can we improve the quality of matched samples? The final paper utilizes the idea of directional penalties, which can improve covariate balance in a matched sample effectively, even for a large observational study.

Notes:

Source: Dissertations Abstracts International, Volume: 82-12, Section: B.

Advisors: Rosenbaum, Paul R.; Committee members: Dylan Small; Jeffrey Silber.

Department: Statistics.

Ph.D. University of Pennsylvania 2021.

Local Notes:

School code: 0175

ISBN:

9798738618260

Access Restriction:

Restricted for use by site license.

This item must not be sold to any third party vendors.