國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : 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.
面頁冊數:	1 online resource (82 pages)
附註:	Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
Contained By:	Dissertations Abstracts International83-01B.
標題:	Bias. -
電子資源:	click for full text (PQDT)
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. - 1 online resource (82 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798534693232Subjects--Topical Terms:

810927
Bias.
Index Terms--Genre/Form:

554714
Electronic books.

Sensitivity Analysis of Unmeasured Confounding in Causal Inference Based on Exponential Tilting and Super Learner.
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504 $a Includes bibliographical references
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.
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