| LDR | | 02622nam u200433 4500 |
| 001 | | 000000419454 |
| 005 | | 20190215163705 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438377424 |
| 035 | |
▼a (MiAaPQ)AAI10840199 |
| 035 | |
▼a (MiAaPQ)duke:14837 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 310 |
| 100 | 1 |
▼a Nguyen, Nghi Le Phuong. |
| 245 | 10 |
▼a Essays on Propensity Score Methods for Causal Inference in Observational Studies. |
| 260 | |
▼a [S.l.]:
▼b Duke University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 124 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 80-02(E), Section: B. |
| 500 | |
▼a Adviser: Fan Li. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Duke University, 2018. |
| 520 | |
▼a In this dissertation, I present three essays from three different research projects and they involve different usages of propensity scores in drawing causal inferences in observational studies. |
| 520 | |
▼a Chapter 1 talks about the general idea of causal inference as well as the concept of randomized experiments and observational studies. It introduces the three different projects and their contributions to the literature. |
| 520 | |
▼a Chapter 2 gives a critical review and an extensive discussion of several commonly-used propensity score methods when the data have a multilevel structure, including matching, weighting, stratification, and methods that combine these with regress |
| 520 | |
▼a In observational studies, subjects are no longer assigned to treatment at random as in randomized experiments, and thus the association between the treatment and outcome can be due to some unmeasured variable that affects both the treatment and |
| 520 | |
▼a Chapter 4 proposes a method for estimating heterogeneous causal effects in observational studies by augmenting additive-interactive Gaussian process regression using the propensity scores, yielding a flexible yet robust way to predict the potent |
| 520 | |
▼a Finally, chapter 5 concludes this dissertation and discusses possible future works for each of the projects. |
| 590 | |
▼a School code: 0066. |
| 650 | 4 |
▼a Statistics. |
| 690 | |
▼a 0463 |
| 710 | 20 |
▼a Duke University.
▼b Statistical Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 80-02B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0066 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14999713
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |