東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Sensitivity Analysis of Unmeasured C...

Zhou, Mi.

FindBook

Google Book

Amazon

博客來

Sensitivity Analysis of Unmeasured Confounding in Causal Inference Based on Exponential Tilting and Super Learner.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Sensitivity Analysis of Unmeasured Confounding in Causal Inference Based on Exponential Tilting and Super Learner./
作者:	Zhou, Mi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	82 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
Contained By:	Dissertations Abstracts International83-01B.
標題:	Generalized linear models. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28544773
ISBN:	9798534693232

Sensitivity Analysis of Unmeasured Confounding in Causal Inference Based on Exponential Tilting and Super Learner.
Zhou, Mi.

Sensitivity Analysis of Unmeasured Confounding in Causal Inference Based on Exponential Tilting and Super Learner. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 82 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: B.

Thesis (Ph.D.)--University of California, Riverside, 2021.

.

Causal inference under the potential outcome framework relies on the strongly ignorable treatment assumption. This assumption is usually unverifiable, and therefore we will never know if there is unmeasured confounding. Unmeasured confounding is one of the fundamental challenges in causal inference. Sensitivity studies are often used to address this issue and evaluate how sensitive a causal estimate is to the unmeasured confounder. In this dissertation, we propose a new sensitivity analysis method to evaluate the impact of the unmeasured confounder by combining ideas of the doubly robust estimator, the exponential tilt method, and the super learner algorithm for both binary and continuous outcomes in chapters 2 and 3, respectively. Compared to other existing methods of sensitivity analysis that parameterize the unmeasured confounder as a latent variable in the working models, the exponential tilting method does not impose any restrictions on the structure or models of the unmeasured confounders. Therefore, the unmeasured confounder could be continuous, binary, or categorical, and could be univariate or multivariate. In order to reduce the modeling bias of traditional parametric methods, we propose incorporating the super learner machine learn-viing algorithm to perform nonparametric model estimation and the corresponding sensitivity analysis. In addition, we employ the data-driven trimming method to handle the estimated extreme propensity scores that can hamper the performance of the proposed doubly robust estimator. Furthermore, most existing sensitivity analysis methods require multivariate sensitivity parameters, which makes it more difficult and subjective to specify a reasonable range of the sensitivity parameters in practice. However, the new method has a univariate sensitivity parameter with a nice and simple interpretation of log-odds ratios and deviation in the conditional means of the outcomes for binary and continuous outcomes respectively. This makes choosing a range for the sensitivity parameter easier for the application. The simulation studies demonstrate the effectiveness of the proposed method.

ISBN: 9798534693232Subjects--Topical Terms:

3561810
Generalized linear models.

Sensitivity Analysis of Unmeasured Confounding in Causal Inference Based on Exponential Tilting and Super Learner.
LDR:03222nmm a2200349 4500 001 2402515
005 20241029122311.5
006 m o d
007 cr#unu||||||||
008 251215s2021 ||||||||||||||||| ||eng d
020 $a 9798534693232
035 $a (MiAaPQ)AAI28544773
035 $a AAI28544773
035 $a 2402515
040 $a MiAaPQ $c MiAaPQ
100 1 $a Zhou, Mi. $3 1263226
245 1 0 $a Sensitivity Analysis of Unmeasured Confounding in Causal Inference Based on Exponential Tilting and Super Learner.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 82 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
500 $a Advisor: Yao, Weixin.
502 $a Thesis (Ph.D.)--University of California, Riverside, 2021.
506 $a .
520 $a Causal inference under the potential outcome framework relies on the strongly ignorable treatment assumption. This assumption is usually unverifiable, and therefore we will never know if there is unmeasured confounding. Unmeasured confounding is one of the fundamental challenges in causal inference. Sensitivity studies are often used to address this issue and evaluate how sensitive a causal estimate is to the unmeasured confounder. In this dissertation, we propose a new sensitivity analysis method to evaluate the impact of the unmeasured confounder by combining ideas of the doubly robust estimator, the exponential tilt method, and the super learner algorithm for both binary and continuous outcomes in chapters 2 and 3, respectively. Compared to other existing methods of sensitivity analysis that parameterize the unmeasured confounder as a latent variable in the working models, the exponential tilting method does not impose any restrictions on the structure or models of the unmeasured confounders. Therefore, the unmeasured confounder could be continuous, binary, or categorical, and could be univariate or multivariate. In order to reduce the modeling bias of traditional parametric methods, we propose incorporating the super learner machine learn-viing algorithm to perform nonparametric model estimation and the corresponding sensitivity analysis. In addition, we employ the data-driven trimming method to handle the estimated extreme propensity scores that can hamper the performance of the proposed doubly robust estimator. Furthermore, most existing sensitivity analysis methods require multivariate sensitivity parameters, which makes it more difficult and subjective to specify a reasonable range of the sensitivity parameters in practice. However, the new method has a univariate sensitivity parameter with a nice and simple interpretation of log-odds ratios and deviation in the conditional means of the outcomes for binary and continuous outcomes respectively. This makes choosing a range for the sensitivity parameter easier for the application. The simulation studies demonstrate the effectiveness of the proposed method.
590 $a School code: 0032.
650 4 $a Generalized linear models. $3 3561810
650 4 $a Variables. $3 3548259
650 4 $a Causality. $3 770986
650 4 $a Probability. $3 518898
650 4 $a Methods. $3 3560391
650 4 $a Bias. $2 gtt $3 1374837
650 4 $a Statistics. $3 517247
650 4 $a Theoretical mathematics. $3 3173530
650 4 $a Applied mathematics. $3 2122814
650 4 $a Standard deviation. $3 3560390
650 4 $a Simulation. $3 644748
650 4 $a Sensitivity analysis. $3 3560752
650 4 $a Parameter identification. $3 3687410
690 $a 0463
690 $a 0642
690 $a 0364
710 2 $a University of California, Riverside. $b Applied Statistics. $3 1676131
773 0 $t Dissertations Abstracts International $g 83-01B.
790 $a 0032
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28544773