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Bayesian Causal Methods for Environmental Accountability Studies with Heterogeneous Effects.

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Bayesian Causal Methods for Environmental Accountability Studies with Heterogeneous Effects.
Συγγραφείς: Zorzetto D; Department of Biostatistics, Brown University, Providence, RI, 02912, USA., Landry E; Department of Biostatistics, University of California Los Angeles, Los Angeles, CA, 90095, USA., Guindani M; Department of Biostatistics, University of California Los Angeles, Los Angeles, CA, 90095, USA., Telesca D; Department of Biostatistics, University of California Los Angeles, Los Angeles, CA, 90095, USA., Edefonti V; Department of Clinical Sciences and Community Health - Dipartimento di Eccellenza 2023-2027, University of Milan, Milan, Italy.; Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy., De Vito R; Department of Biostatistics, Brown University, Providence, RI, 02912, USA. roberta.devito@uniroma1.it.; Department of Statistics, Sapienza University of Rome, Rome, Italy. roberta.devito@uniroma1.it., Bargagli-Stoffi FJ; Department of Biostatistics, University of California Los Angeles, Los Angeles, CA, 90095, USA.
Πηγή: Current environmental health reports [Curr Environ Health Rep] 2026 Sep 17; Vol. 13 (1). Date of Electronic Publication: 2026 Sep 17.
Τύπος έκδοσης: Journal Article; Review
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Springer International Publishing AG Country of Publication: Switzerland NLM ID: 101629387 Publication Model: Electronic Cited Medium: Internet ISSN: 2196-5412 (Electronic) Linking ISSN: 21965412 NLM ISO Abbreviation: Curr Environ Health Rep Subsets: MEDLINE
Imprint Name(s): Original Publication: [Switzerland] : Springer International Publishing AG, [2014]-
Ιατρικοί όροι (MeSH): Environmental Exposure* , Social Responsibility*, Bayes Theorem ; Humans ; Air Pollution ; Monte Carlo Method
Περίληψη: Purpose of Review: Environmental accountability studies evaluate whether regulatory interventions achieve their intended benefits. These studies fall into two broad categories: indirect accountability, which estimates the effects of exposure to higher levels of air pollution and uses them to indirectly predict policy impacts, and direct accountability, which evaluates specific interventions directly. The purpose of this review is to evaluate recent advances in causal inference and flexible Bayesian statistical modeling to support environmental accountability studies.
Recent Findings: Recent studies, in both direct and indirect environmental accountability, deeply rely on causal inference to produce robust inferences and provide relevant and actionable insights to policy-makers and practitioners. In this context, heterogeneous treatment effects are central to both approaches as it is critically important to understand which subpopulations are most vulnerable (or resilient), requiring methods that can characterize effect variation. Bayesian nonparametric (BNP) methods have been proposed for this task due to their flexibility and ability to cluster units into coherent subgroups. We present a selection of BNP methods designed or adapted for each type of accountability, focusing on complementary approaches based on Bayesian Additive Regression Trees (BART) and Dependent Dirichlet Processes (DDP). Through Monte Carlo simulations, we compare these methods, identify their respective advantages, and provide recommendations for adoption and implementation in environmental accountability research.
(© 2026. The Author(s).)
Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: Not applicable. Human and Animal Rights and Informed Consent: This article does not contain studies with human or animal subjects performed by any of the authors.
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Biostatistics. 2015 Jul;16(3):493-508. (PMID: 25532525)
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J Causal Inference. 2026;14(1):. (PMID: 41725901)
J R Stat Soc Series B Stat Methodol. 2009 Nov 1;71(5):905-925. (PMID: 20694043)
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Biometrics. 2024 Mar 27;80(2):. (PMID: 38640436)
Nature. 2022 Jan;601(7892):228-233. (PMID: 35022594)
Biostatistics. 2012 Apr;13(2):289-302. (PMID: 22267524)
Am Stat. 2016 Mar 31;70(1):47-54. (PMID: 27482121)
Epidemiology. 2013 Nov;24(6):809-19. (PMID: 24045717)
J Open Source Softw. 2023;8(92):. (PMID: 40735006)
Sci Adv. 2020 Jul 17;6(29):eaba5692. (PMID: 32832626)
Am J Epidemiol. 2014 Jul 1;180(1):15-28. (PMID: 24872350)
JAMA. 2002 Mar 6;287(9):1132-41. (PMID: 11879110)
Epidemiology. 2018 Mar;29(2):165-174. (PMID: 29095246)
Am J Epidemiol. 2014 Dec 15;180(12):1133-40. (PMID: 25399414)
Stat Methods Med Res. 2012 Feb;21(1):31-54. (PMID: 21030422)
Ann Appl Stat. 2011;5(3):1876-1892. (PMID: 22308190)
Biostatistics. 2026 Jan 20;27(1):. (PMID: 42296463)
Annu Rev Public Health. 2016;37:47-60. (PMID: 26789381)
Epidemiology. 2016 May;27(3):368-77. (PMID: 26841057)
J R Stat Soc Series B Stat Methodol. 2017 Sep;79(4):1229-1245. (PMID: 28989320)
Proc Natl Acad Sci U S A. 2016 Jul 5;113(27):7353-60. (PMID: 27382149)
N Engl J Med. 2017 Jun 29;376(26):2513-2522. (PMID: 28657878)
Wiley Interdiscip Rev Comput Stat. 2023 Jan-Feb;15(1):. (PMID: 36713955)
Int J Epidemiol. 2026 Apr 17;55(3):. (PMID: 42287695)
Stat Med. 2021 Jan 30;40(2):518-551. (PMID: 33015870)
Ann Appl Stat. 2025 Sep;19(3):1847-1867. (PMID: 42318454)
Annu Rev Public Health. 2019 Apr 1;40:23-43. (PMID: 30633715)
Biometrics. 2025 Jan 07;81(1):. (PMID: 40094165)
Biometrics. 2019 Sep;75(3):778-787. (PMID: 30859545)
Eur J Epidemiol. 2025 Feb;40(2):141-150. (PMID: 40038141)
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J Am Stat Assoc. 2024;119(545):757-772. (PMID: 38524247)
J R Stat Soc Series B Stat Methodol. 2008 Sep 1;70(4):825-848. (PMID: 19750209)
Bayesian Anal. 2011 Mar 1;6(1):. (PMID: 24358072)
N Engl J Med. 2023 Apr 13;388(15):1396-1404. (PMID: 36961127)
Proc Natl Acad Sci U S A. 2019 Mar 26;116(13):6001-6006. (PMID: 30858319)
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Environ Health. 2018 Aug 20;17(1):67. (PMID: 30126431)
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J Comput Graph Stat. 2024;33(4):1139-1149. (PMID: 39651451)
Contributed Indexing: Keywords: Bayesian nonparametrics; Causal inference; Environmental accountability; Heterogeneous treatment effects
Entry Date(s): Date Created: 20260917 Date Completed: 20260917 Latest Revision: 20260920
Update Code: 20260920
PubMed Central ID: PMC13585896
DOI: 10.1007/s40572-026-00555-5
PMID: 42752745
Βάση Δεδομένων: MEDLINE
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  Data: <searchLink fieldCode="AU" term="%22Zorzetto+D%22">Zorzetto D</searchLink>; Department of Biostatistics, Brown University, Providence, RI, 02912, USA.<br /><searchLink fieldCode="AU" term="%22Landry+E%22">Landry E</searchLink>; Department of Biostatistics, University of California Los Angeles, Los Angeles, CA, 90095, USA.<br /><searchLink fieldCode="AU" term="%22Guindani+M%22">Guindani M</searchLink>; Department of Biostatistics, University of California Los Angeles, Los Angeles, CA, 90095, USA.<br /><searchLink fieldCode="AU" term="%22Telesca+D%22">Telesca D</searchLink>; Department of Biostatistics, University of California Los Angeles, Los Angeles, CA, 90095, USA.<br /><searchLink fieldCode="AU" term="%22Edefonti+V%22">Edefonti V</searchLink>; Department of Clinical Sciences and Community Health - Dipartimento di Eccellenza 2023-2027, University of Milan, Milan, Italy.; Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico, Milan, Italy.<br /><searchLink fieldCode="AU" term="%22De+Vito+R%22">De Vito R</searchLink>; Department of Biostatistics, Brown University, Providence, RI, 02912, USA. roberta.devito@uniroma1.it.; Department of Statistics, Sapienza University of Rome, Rome, Italy. roberta.devito@uniroma1.it.<br /><searchLink fieldCode="AU" term="%22Bargagli-Stoffi+FJ%22">Bargagli-Stoffi FJ</searchLink>; Department of Biostatistics, University of California Los Angeles, Los Angeles, CA, 90095, USA.
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  Data: Purpose of Review: Environmental accountability studies evaluate whether regulatory interventions achieve their intended benefits. These studies fall into two broad categories: indirect accountability, which estimates the effects of exposure to higher levels of air pollution and uses them to indirectly predict policy impacts, and direct accountability, which evaluates specific interventions directly. The purpose of this review is to evaluate recent advances in causal inference and flexible Bayesian statistical modeling to support environmental accountability studies.<br />Recent Findings: Recent studies, in both direct and indirect environmental accountability, deeply rely on causal inference to produce robust inferences and provide relevant and actionable insights to policy-makers and practitioners. In this context, heterogeneous treatment effects are central to both approaches as it is critically important to understand which subpopulations are most vulnerable (or resilient), requiring methods that can characterize effect variation. Bayesian nonparametric (BNP) methods have been proposed for this task due to their flexibility and ability to cluster units into coherent subgroups. We present a selection of BNP methods designed or adapted for each type of accountability, focusing on complementary approaches based on Bayesian Additive Regression Trees (BART) and Dependent Dirichlet Processes (DDP). Through Monte Carlo simulations, we compare these methods, identify their respective advantages, and provide recommendations for adoption and implementation in environmental accountability research.<br /> (© 2026. The Author(s).)
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  Data: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: Not applicable. Human and Animal Rights and Informed Consent: This article does not contain studies with human or animal subjects performed by any of the authors.
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          Titles:
            – TitleFull: Current environmental health reports
              Type: main
ResultId 1