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			<depositor_name>Institute for Advanced Materials Research Press</depositor_name>
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				<full_title>Journal of Artificial Intelligence for Materials Science</full_title>
				<abbrev_title>J. Artif. Intell. Mater. Sci.</abbrev_title>
				<issn>3149-8957</issn>
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				<publication_date>
					<year>2024</year>
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					<volume>3</volume>
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				<issue>2</issue>
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					<title>Conceptual Treatments of Causality in Materials Informatics — From Correlation to Intervention: A Review Study</title>
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          					<person_name sequence="first" contributor_role="author">
            <given_name>Anna</given_name>
            <surname>Kowalska</surname>
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            <given_name>Piotr</given_name>
            <surname>Nowak</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Tomasz</given_name>
            <surname>Zielinski</surname>
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          					<person_name sequence="additional" contributor_role="author">
            <given_name>Katarzyna</given_name>
            <surname>Mazur</surname>
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								<publication_date>
					<year>2024</year>
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					  <unstructured_citation>He H, Zhang H, Roth D. Rethinking with retrieval: Faithful large language model inference. arXiv [Preprint]. 2022:arXiv:2301.00303.</unstructured_citation>
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