國立虎尾科技大學 |

Causal Inference for High-Stakes Decisions.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Causal Inference for High-Stakes Decisions./
Author:	Parikh, Harsh J.
Description:	1 online resource (152 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798379571252

Causal Inference for High-Stakes Decisions.
Parikh, Harsh J.

Causal Inference for High-Stakes Decisions. - 1 online resource (152 pages)

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

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

Includes bibliographical references

Causal inference methods are commonly used across domains to aid high-stakes decision-making. The validity of causal studies often relies on strong assumptions that might not be realistic in high-stakes scenarios. Inferences based on incorrect assumptions frequently result in sub-optimal decisions with high penalties and long-term consequences. Unlike prediction or machine learning methods, it is particularly challenging to evaluate the performance of causal methods using just the observed data because the ground truth causal effects are missing for all units. My research presents frameworks to enable validation of causal inference methods in one of the following three ways: (i) auditing the estimation procedure by a domain expert, (ii) studying the performance using synthetic data, and (iii) using placebo tests to identify biases. This work enables decision-makers to reason about the validity of the estimation procedure by thinking carefully about the underlying assumptions. Our Learning-to-Match framework is an auditable-and-accurate approach that learns an optimal distance metric for estimating heterogeneous treatment effects. We augment Learning-to-Match framework with pharmacological mechanistic knowledge to study the long-term effects of untreated seizure-like brain activities in critically ill patients. Here, the auditability of the estimator allowed neurologists to qualitatively validate the analysis via a chart-review. We also propose Credence, a synthetic data based framework to validate causal inference methods. Credence simulates data that is stochastically indistinguishable from the observed data while allowing for user-designed treatment effects and selection biases. We demonstrate Credence's ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications. We also discuss an approach to combines experimental and observational studies. Our approach provides a principled approach to test for the violations of key assumptions and estimate causal effects (Chapter 5).

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

Mode of access: World Wide Web

ISBN: 9798379571252Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Causal inferenceIndex Terms--Genre/Form:

554714
Electronic books.

Causal Inference for High-Stakes Decisions.
LDR:03468ntm a22004097 4500 001 1150075
005 20241022111536.5
006 m o d
007 cr bn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379571252
035 $a (MiAaPQ)AAI30311291
035 $a AAI30311291
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Parikh, Harsh J. $3 1476495
245 1 0 $a Causal Inference for High-Stakes Decisions.
264 0 $c 2023
300 $a 1 online resource (152 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
500 $a Advisor: Rudin, Cynthia;Roy, Sudeepa.
502 $a Thesis (Ph.D.)--Duke University, 2023.
504 $a Includes bibliographical references
520 $a Causal inference methods are commonly used across domains to aid high-stakes decision-making. The validity of causal studies often relies on strong assumptions that might not be realistic in high-stakes scenarios. Inferences based on incorrect assumptions frequently result in sub-optimal decisions with high penalties and long-term consequences. Unlike prediction or machine learning methods, it is particularly challenging to evaluate the performance of causal methods using just the observed data because the ground truth causal effects are missing for all units. My research presents frameworks to enable validation of causal inference methods in one of the following three ways: (i) auditing the estimation procedure by a domain expert, (ii) studying the performance using synthetic data, and (iii) using placebo tests to identify biases. This work enables decision-makers to reason about the validity of the estimation procedure by thinking carefully about the underlying assumptions. Our Learning-to-Match framework is an auditable-and-accurate approach that learns an optimal distance metric for estimating heterogeneous treatment effects. We augment Learning-to-Match framework with pharmacological mechanistic knowledge to study the long-term effects of untreated seizure-like brain activities in critically ill patients. Here, the auditability of the estimator allowed neurologists to qualitatively validate the analysis via a chart-review. We also propose Credence, a synthetic data based framework to validate causal inference methods. Credence simulates data that is stochastically indistinguishable from the observed data while allowing for user-designed treatment effects and selection biases. We demonstrate Credence's ability to accurately assess the relative performance of causal estimation techniques in an extensive simulation study and two real-world data applications. We also discuss an approach to combines experimental and observational studies. Our approach provides a principled approach to test for the violations of key assumptions and estimate causal effects (Chapter 5).
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Statistics. $3 556824
653 $a Causal inference
653 $a Causality
653 $a Decision making
653 $a Econometrics
653 $a Interpretable
653 $a Machine learning
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0463
690 $a 0501
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Duke University. $b Computer Science. $3 1190730
773 0 $t Dissertations Abstracts International $g 84-11B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30311291 $z click for full text (PQDT)