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Optimal Transport Methods for Causal Inference, Multisample Testing, and Model Interpretation.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimal Transport Methods for Causal Inference, Multisample Testing, and Model Interpretation./
作者:	Dunipace, Eric A.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	199 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
Contained By:	Dissertations Abstracts International83-01B.
標題:	Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28498326
ISBN:	9798534671827

Optimal Transport Methods for Causal Inference, Multisample Testing, and Model Interpretation.
Dunipace, Eric A.

Optimal Transport Methods for Causal Inference, Multisample Testing, and Model Interpretation. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 199 p.

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

Thesis (Ph.D.)--Harvard University, 2021.

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

The manuscript discusses three topics that utilize optimal transport and related methodologies to solve problems in statistics. Chapter 2 uses the Wasserstein distance to construct interpretable approximations to complicated models, Chapter 3 uses optimal transport distances to construct weighting estimators for causal inference, and Chapter 4 uses Hamiltonian paths and nearest neighbor graphs for multivariate testing. Each chapter is self-contained and the corresponding abstracts are given below.Chapter 2: Statistical models often include thousands of parameters. However, large models decrease the investigator's ability to interpret and communicate the estimated parameters. Reducing the dimensionality of the parameter space in the estimation phase is a commonly used approach, but less work has focused on selecting subsets of the parameters for interpreting the estimated model - especially in settings such as Bayesian inference and model averaging. Importantly, many models do not have straightforward interpretations and create another layer of obfuscation. To solve this gap, we introduce a new method that uses the Wasserstein distance to identify a low-dimensional interpretable model projection. After the estimation of complex models, users can budget how many parameters they wish to interpret and the proposed generates a simplified model of the desired dimension minimizing the distance to the full model. We provide simulation results to illustrate the method and apply it to cancer datasets.Chapter 3: Weighting methods are a common tool to de-bias estimates of causal effects. And though there are an increasing number of seemingly disparate methods, many of them can be folded into one unifying regime: causal optimal transport. This new method directly targets distributional balance by minimizing optimal transport distances between treatment and control groups or, more generally, between a source and target population. Our approach is model-free but can also incorporate moments or any other important functions of covariates that the re- searcher desires to balance. We find that the causal optimal transport outperforms competitor methods when both the propensity score and outcome models are misspecified, indicating it is a robust alternative to common weighting methods. Finally, we demonstrate the utility of our method in an external control study examining the effect of misoprostol versus oxytocin for treatment of post-partum hemorrhage.Chapter 4: We propose non-parametric, graph-based tests to assess the distributional balance of covariates in observational studies with multi-valued treatments. Our tests utilize graph structures ranging from Hamiltonian paths that connect all of the data to nearest neighbor graphs that maximally separates data into pairs. We consider algorithms that form minimal distance graphs, such as optimal Hamiltonian paths or non-bipartite matching, or approximate alternatives, such as greedy Hamiltonian paths or greedy nearest neighbor graphs. Extensive simulation studies demonstrate that the proposed tests are able to detect the misspecification of matching models that other methods miss. Contrary to intuition, we also find that tests ran on well-formed ap- proximate graphs do better in most cases than tests run on optimally formed graphs, and that a properly formed test on an approximate nearest neighbor graph performs best, on average. In a multi-valued treatment setting with breast cancer data, these graph-based tests can also detect imbalances otherwise missed by common matching diagnostics. We provide a new R package multivariateTesting to implement these methods and reproduce our results.

ISBN: 9798534671827Subjects--Topical Terms:

1002712
Biostatistics.
Subjects--Index Terms:

Causal inference

Optimal Transport Methods for Causal Inference, Multisample Testing, and Model Interpretation.
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100 1 $a Dunipace, Eric A. $0 (orcid)0000-0001-8909-213X $3 3688956
245 1 0 $a Optimal Transport Methods for Causal Inference, Multisample Testing, and Model Interpretation.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 199 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-01, Section: B.
500 $a Advisor: Zubizarreta, Jose;Trippa, Lorenzo.
502 $a Thesis (Ph.D.)--Harvard University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a The manuscript discusses three topics that utilize optimal transport and related methodologies to solve problems in statistics. Chapter 2 uses the Wasserstein distance to construct interpretable approximations to complicated models, Chapter 3 uses optimal transport distances to construct weighting estimators for causal inference, and Chapter 4 uses Hamiltonian paths and nearest neighbor graphs for multivariate testing. Each chapter is self-contained and the corresponding abstracts are given below.Chapter 2: Statistical models often include thousands of parameters. However, large models decrease the investigator's ability to interpret and communicate the estimated parameters. Reducing the dimensionality of the parameter space in the estimation phase is a commonly used approach, but less work has focused on selecting subsets of the parameters for interpreting the estimated model - especially in settings such as Bayesian inference and model averaging. Importantly, many models do not have straightforward interpretations and create another layer of obfuscation. To solve this gap, we introduce a new method that uses the Wasserstein distance to identify a low-dimensional interpretable model projection. After the estimation of complex models, users can budget how many parameters they wish to interpret and the proposed generates a simplified model of the desired dimension minimizing the distance to the full model. We provide simulation results to illustrate the method and apply it to cancer datasets.Chapter 3: Weighting methods are a common tool to de-bias estimates of causal effects. And though there are an increasing number of seemingly disparate methods, many of them can be folded into one unifying regime: causal optimal transport. This new method directly targets distributional balance by minimizing optimal transport distances between treatment and control groups or, more generally, between a source and target population. Our approach is model-free but can also incorporate moments or any other important functions of covariates that the re- searcher desires to balance. We find that the causal optimal transport outperforms competitor methods when both the propensity score and outcome models are misspecified, indicating it is a robust alternative to common weighting methods. Finally, we demonstrate the utility of our method in an external control study examining the effect of misoprostol versus oxytocin for treatment of post-partum hemorrhage.Chapter 4: We propose non-parametric, graph-based tests to assess the distributional balance of covariates in observational studies with multi-valued treatments. Our tests utilize graph structures ranging from Hamiltonian paths that connect all of the data to nearest neighbor graphs that maximally separates data into pairs. We consider algorithms that form minimal distance graphs, such as optimal Hamiltonian paths or non-bipartite matching, or approximate alternatives, such as greedy Hamiltonian paths or greedy nearest neighbor graphs. Extensive simulation studies demonstrate that the proposed tests are able to detect the misspecification of matching models that other methods miss. Contrary to intuition, we also find that tests ran on well-formed ap- proximate graphs do better in most cases than tests run on optimally formed graphs, and that a properly formed test on an approximate nearest neighbor graph performs best, on average. In a multi-valued treatment setting with breast cancer data, these graph-based tests can also detect imbalances otherwise missed by common matching diagnostics. We provide a new R package multivariateTesting to implement these methods and reproduce our results.
590 $a School code: 0084.
650 4 $a Biostatistics. $3 1002712
650 4 $a Statistics. $3 517247
650 4 $a Computer science. $3 523869
650 4 $a Standard deviation. $3 3560390
650 4 $a Simulation. $3 644748
650 4 $a Experiments. $3 525909
650 4 $a Generalized linear models. $3 3561810
650 4 $a Neural networks. $3 677449
650 4 $a Algorithms. $3 536374
650 4 $a Performance evaluation. $3 3562292
650 4 $a Case studies. $2 itrt $3 996239
653 $a Causal inference
653 $a Model interpretation
653 $a Multisample testing
653 $a Optimal transport
653 $a Wasserstein distance
690 $a 0308
690 $a 0463
690 $a 0984
710 2 $a Harvard University. $b Biostatistics. $3 2104931
773 0 $t Dissertations Abstracts International $g 83-01B.
790 $a 0084
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28498326