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Causal Inferences from Digital Behavioral Data

delete2026-02-01
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OA
AI
H
Heinz Leitgöb *
F
Florian Keusch
DOI：10.1007/s11577-026-01050-3delete
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摘要

摘要

En 中文
近年来，数字行为数据(DBD)已成为社会科学研究中的强大资源。它们的普遍性、细粒度、复杂性和连续收集为详细考察社会过程提供了新的机会。然而，由于DBD类型多样且通常构成现成数据（非为研究目的而生成的数据），其因果分析潜力常被低估。为解决此问题，本文概述了开发使用DBD进行有效因果推断的方法论框架的关键考量。讨论重点在于设计局限性如何：(i)在设计DBD时预先排除，或(ii)通过理论及时序信息、结构因果模型的具体化、事后设计考量以及适当分析工具的应用进行补偿，使现成DBD适用于因果效应估计。
Keyword:
Digital behavioral data
Causal inference
Structural causal models
Causal machine learning
Generative artificial intelligence
Computational social science
Digitale Verhaltensdaten
Kausale Inferenz
Strukturelle Kausalmodelle
Kausales maschinelles Lernen
Generative k & uuml
nstliche Intelligenz
Computationale Sozialwissenschaften
AI总结

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期刊

K
KOLNER ZEITSCHRIFT FUR SOZIOLOGIE UND SOZIALPSYCHOLOGIE
IF:
0.9
论文数:
63
被引数:
890

机构

U
university of mannheim
学者数:
269
论文数: 180
被引数: 0
L
leipzig university
学者数:
1.8K
论文数: 759
被引数: 0
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