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Causal Inference with Non-Standard E...

Wu, Han.

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Causal Inference with Non-Standard Experimental Designs.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Causal Inference with Non-Standard Experimental Designs./
作者:	Wu, Han.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	165 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: A.
Contained By:	Dissertations Abstracts International85-04A.
標題:	Design. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30614627
ISBN:	9798380481496

Causal Inference with Non-Standard Experimental Designs.
Wu, Han.

Causal Inference with Non-Standard Experimental Designs. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 165 p.

Source: Dissertations Abstracts International, Volume: 85-04, Section: A.

Thesis (Ph.D.)--Stanford University, 2023.

The past decades have seen a comprehensive body of research dedicated to causal inference in conventional experimental designs. However, as technological innovations continue to foster a rapid influx of data across numerous fields, the datasets derived often exhibit new structures that stem from unconventional designs. The thesis at hand is centered around the development of methods for conducting causal inference, particularly when the design deviates from the standard, thereby making conventional methods inapplicable.Chapter 2 delves into the regression discontinuity design in cases where the running variable is a noisy measurement of a latent variable. We propose a novel design-based approach for estimation and inference. This approach proves effective when applied to a broad array of widely-used estimands.Chapter 3 explores adaptive experimentation in the context of delayed feedback. In subchapter 3.1, we extend Thompson sampling to the proportional hazard model and develop a method capable of overcoming challenges associated with vaccine trials. Subsequently, in subchapter 3.2, we study the behavior of Thompson sampling when delays are unrestricted, providing theoretical regret bounds and conducting extensive experiments.Chapter 4 investigates policy learning in scenarios involving multiple treatments or multiple outcomes. In subchapter 4.1, we propose methods for evaluating policies when cost constraints accompany multiple treatments. In subchapter 4.2, we introduce a personalized experimentation system that can learn interpretable policies from experimental data and is scalable for big datasets.

ISBN: 9798380481496Subjects--Topical Terms:

518875
Design.

Causal Inference with Non-Standard Experimental Designs.
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