Pub Date : 2025-04-22DOI: 10.1177/00491241251314037
John W. Jackson, Yea-Jen Hsu, Raquel C. Greer, Romsai T. Boonyasai, Chanelle J. Howe
We present a conceptual model to measure disparity—the target study—where social groups may be similarly situated (i.e., balanced) on allowable covariates. Our model, based on a sampling design, does not intervene to assign social group membership or alter allowable covariates. To address nonrandom sample selection, we extend our model to generalize or transport disparity or to assess disparity after an intervention on eligibility-related variables that eliminates forms of collider-stratification. To avoid bias from differential timing of enrollment, we aggregate time-specific study results by balancing calendar time of enrollment across social groups. To provide a framework for emulating our model, we discuss study designs, data structures, and G-computation and weighting estimators. We compare our sampling-based model to prominent decomposition-based models used in healthcare and algorithmic fairness. We provide R code for all estimators and apply our methods to measure health system disparities in hypertension control using electronic medical records.
{"title":"The Target Study: A Conceptual Model and Framework for Measuring Disparity","authors":"John W. Jackson, Yea-Jen Hsu, Raquel C. Greer, Romsai T. Boonyasai, Chanelle J. Howe","doi":"10.1177/00491241251314037","DOIUrl":"https://doi.org/10.1177/00491241251314037","url":null,"abstract":"We present a conceptual model to measure disparity—the target study—where social groups may be similarly situated (i.e., balanced) on allowable covariates. Our model, based on a sampling design, does not intervene to assign social group membership or alter allowable covariates. To address nonrandom sample selection, we extend our model to generalize or transport disparity or to assess disparity after an intervention on eligibility-related variables that eliminates forms of collider-stratification. To avoid bias from differential timing of enrollment, we aggregate time-specific study results by balancing calendar time of enrollment across social groups. To provide a framework for emulating our model, we discuss study designs, data structures, and G-computation and weighting estimators. We compare our sampling-based model to prominent decomposition-based models used in healthcare and algorithmic fairness. We provide R code for all estimators and apply our methods to measure health system disparities in hypertension control using electronic medical records.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"26 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143863022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-22DOI: 10.1177/00491241251321152
Chen-Shuo Hong
Social networks literature has explored homophily, the tendency to associate with similar others, as a critical boundary-making process contributing to segregated networks along the lines of identities. Yet, social network research generally conceptualizes identities as sociodemographic categories and seldom considers the inherently continuous and heterogeneous nature of differences. Drawing upon the infracategorical model of inequality, this study demonstrates that a computational approach – combining machine learning and exponential random graph models (ERGMs) – can capture the role of categorical conformity in network structures. Through a case study of gender segregation in friendships, this study presents a workflow for developing a machine-learning-based gender conformity measure and applying it to guide the social network analysis of cultural matching. Results show that adolescents with similar gender conformity are more likely to form friendships, net of homophily based on categorical gender and other controls, and homophily by gender conformity mediates homophily by categorical gender. The study concludes by discussing the limitations of this computational approach and its unique strengths in enhancing theories on categories, boundaries, and stratification.
{"title":"Networks Beyond Categories: A Computational Approach to Examining Gender Homophily","authors":"Chen-Shuo Hong","doi":"10.1177/00491241251321152","DOIUrl":"https://doi.org/10.1177/00491241251321152","url":null,"abstract":"Social networks literature has explored homophily, the tendency to associate with similar others, as a critical boundary-making process contributing to segregated networks along the lines of identities. Yet, social network research generally conceptualizes identities as sociodemographic categories and seldom considers the inherently continuous and heterogeneous nature of differences. Drawing upon the infracategorical model of inequality, this study demonstrates that a computational approach – combining machine learning and exponential random graph models (ERGMs) – can capture the role of categorical conformity in network structures. Through a case study of gender segregation in friendships, this study presents a workflow for developing a machine-learning-based gender conformity measure and applying it to guide the social network analysis of cultural matching. Results show that adolescents with similar gender conformity are more likely to form friendships, net of homophily based on categorical gender and other controls, and homophily by gender conformity mediates homophily by categorical gender. The study concludes by discussing the limitations of this computational approach and its unique strengths in enhancing theories on categories, boundaries, and stratification.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"32 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-22DOI: 10.1177/00491241251326865
David Broska, Michael Howes, Austin van Loon
Large language models (LLMs) provide cost-effective but possibly inaccurate predictions of human behavior. Despite growing evidence that predicted and observed behavior are often not interchangeable , there is limited guidance on using LLMs to obtain valid estimates of causal effects and other parameters. We argue that LLM predictions should be treated as potentially informative observations, while human subjects serve as a gold standard in a mixed subjects design . This paradigm preserves validity and offers more precise estimates at a lower cost than experiments relying exclusively on human subjects. We demonstrate—and extend—prediction-powered inference (PPI), a method that combines predictions and observations. We define the PPI correlation as a measure of interchangeability and derive the effective sample size for PPI. We also introduce a power analysis to optimally choose between informative but costly human subjects and less informative but cheap predictions of human behavior. Mixed subjects designs could enhance scientific productivity and reduce inequality in access to costly evidence.
{"title":"The Mixed Subjects Design: Treating Large Language Models as Potentially Informative Observations","authors":"David Broska, Michael Howes, Austin van Loon","doi":"10.1177/00491241251326865","DOIUrl":"https://doi.org/10.1177/00491241251326865","url":null,"abstract":"Large language models (LLMs) provide cost-effective but possibly inaccurate predictions of human behavior. Despite growing evidence that predicted and observed behavior are often not <jats:italic>interchangeable</jats:italic> , there is limited guidance on using LLMs to obtain valid estimates of causal effects and other parameters. We argue that LLM predictions should be treated as potentially informative observations, while human subjects serve as a gold standard in a <jats:italic>mixed subjects design</jats:italic> . This paradigm preserves validity and offers more precise estimates at a lower cost than experiments relying exclusively on human subjects. We demonstrate—and extend—prediction-powered inference (PPI), a method that combines predictions and observations. We define the <jats:italic>PPI correlation</jats:italic> as a measure of interchangeability and derive the <jats:italic>effective sample size</jats:italic> for PPI. We also introduce a power analysis to optimally choose between <jats:italic>informative but costly</jats:italic> human subjects and <jats:italic>less informative but cheap</jats:italic> predictions of human behavior. Mixed subjects designs could enhance scientific productivity and reduce inequality in access to costly evidence.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"4 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143862886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-21DOI: 10.1177/00491241251320963
Lai Wei, Yu Xie
The study of mobility effects is an important subject of study in sociology. Empirical investigations of individual mobility effects, however, have been hindered by one fundamental limitation, the unidentifiability of mobility effects when origin and destination are held constant. Given this fundamental limitation, we propose to reconceptualize mobility effects from the micro- to macro-level. Instead of micro-level mobility effects, the primary focus of the past literature, we ask alternative research questions about macro-level mobility effects: What happens to the population distribution of an outcome if we manipulate the mobility regime, that is, if we alter the observed association between social origin and social destination? We relate individual-level mobility experience to macro-level mobility effects under special interventions. The proposed method bridges the macro and micro agendas in social stratification research, and has wider applications in social stratification beyond the study of mobility effects. We illustrate the method with two analyses that evaluate the impact of social mobility on average fertility and income inequality in the United States. We provide an open-source software, the R package socmob , that implements the method.
流动效应研究是社会学的一个重要研究课题。然而，对个人流动效应的实证研究却受到一个基本限制的阻碍，即在原籍地和目的地不变的情况下，流动效应是不可识别的。鉴于这一基本限制，我们建议从微观到宏观层面重新认识流动效应。与以往文献主要关注的微观层面的流动效应不同，我们提出了有关宏观层面流动效应的其他研究问题：如果我们操纵流动制度，也就是说，如果我们改变观察到的社会原籍地和社会目的地之间的关联，结果的人口分布会发生什么变化？我们将个人层面的流动经验与特殊干预下的宏观流动效应联系起来。所提出的方法在社会分层研究的宏观和微观议程之间架起了一座桥梁，在流动效应研究之外的社会分层领域也有更广泛的应用。我们用两个分析来说明该方法，这两个分析评估了社会流动性对美国平均生育率和收入不平等的影响。我们提供了一个开源软件，即实现该方法的 R 软件包 socmob。
{"title":"Social Mobility as Causal Intervention","authors":"Lai Wei, Yu Xie","doi":"10.1177/00491241251320963","DOIUrl":"https://doi.org/10.1177/00491241251320963","url":null,"abstract":"The study of mobility effects is an important subject of study in sociology. Empirical investigations of individual mobility effects, however, have been hindered by one fundamental limitation, the unidentifiability of mobility effects when origin and destination are held constant. Given this fundamental limitation, we propose to reconceptualize mobility effects from the micro- to macro-level. Instead of micro-level mobility effects, the primary focus of the past literature, we ask alternative research questions about macro-level mobility effects: What happens to the population distribution of an outcome if we manipulate the mobility regime, that is, if we alter the observed association between social origin and social destination? We relate individual-level mobility experience to macro-level mobility effects under special interventions. The proposed method bridges the macro and micro agendas in social stratification research, and has wider applications in social stratification beyond the study of mobility effects. We illustrate the method with two analyses that evaluate the impact of social mobility on average fertility and income inequality in the United States. We provide an open-source software, the R package <jats:italic>socmob</jats:italic> , that implements the method.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"1 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-21DOI: 10.1177/00491241251333372
Alessandra Rister Portinari Maranca, Jihoon Chung, Musashi Hinck, Adam D. Wolsky, Naoki Egami, Brandon M. Stewart
Generative artificial intelligence (AI) has shown incredible leaps in performance across data of a variety of modalities including texts, images, audio, and videos. This affords social scientists the ability to annotate variables of interest from unstructured media. While rapidly improving, these methods are far from perfect and, as we show, even ignoring the small amounts of error in high accuracy systems can lead to substantial bias and invalid confidence intervals in downstream analysis. We review how using design-based supervised learning (DSL) guarantees asymptotic unbiasedness and proper confidence interval coverage by making use of a small number of expert annotations. While originally developed for use with large language models in text, we present a series of applications in the context of image analysis, including an investigation of visual predictors of the perceived level of violence in protest images, an analysis of the images shared in the Black Lives Matter movement on Twitter, and a study of U.S. outlets reporting of immigrant caravans. These applications are representative of the type of analysis performed in the visual social science landscape today, and our analyses will exemplify how DSL helps us attain statistical guarantees while using automated methods to reduce human labor.
{"title":"Correcting the Measurement Errors of AI-Assisted Labeling in Image Analysis Using Design-Based Supervised Learning","authors":"Alessandra Rister Portinari Maranca, Jihoon Chung, Musashi Hinck, Adam D. Wolsky, Naoki Egami, Brandon M. Stewart","doi":"10.1177/00491241251333372","DOIUrl":"https://doi.org/10.1177/00491241251333372","url":null,"abstract":"Generative artificial intelligence (AI) has shown incredible leaps in performance across data of a variety of modalities including texts, images, audio, and videos. This affords social scientists the ability to annotate variables of interest from unstructured media. While rapidly improving, these methods are far from perfect and, as we show, even ignoring the small amounts of error in high accuracy systems can lead to substantial bias and invalid confidence intervals in downstream analysis. We review how using design-based supervised learning (DSL) guarantees asymptotic unbiasedness and proper confidence interval coverage by making use of a small number of expert annotations. While originally developed for use with large language models in text, we present a series of applications in the context of image analysis, including an investigation of visual predictors of the perceived level of violence in protest images, an analysis of the images shared in the Black Lives Matter movement on Twitter, and a study of U.S. outlets reporting of immigrant caravans. These applications are representative of the type of analysis performed in the visual social science landscape today, and our analyses will exemplify how DSL helps us attain statistical guarantees while using automated methods to reduce human labor.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"3 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143857722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-21DOI: 10.1177/00491241251330582
Julien Boelaert, Samuel Coavoux, Étienne Ollion, Ivaylo Petev, Patrick Präg
Generative artificial intelligence (AI) is increasingly presented as a potential substitute for humans, including as research subjects. However, there is no scientific consensus on how closely these in silico clones can emulate survey respondents. While some defend the use of these “synthetic users,” others point toward social biases in the responses provided by large language models (LLMs). In this article, we demonstrate that these critics are right to be wary of using generative AI to emulate respondents, but probably not for the right reasons. Our results show (i) that to date, models cannot replace research subjects for opinion or attitudinal research; (ii) that they display a strong bias and a low variance on each topic; and (iii) that this bias randomly varies from one topic to the next. We label this pattern “machine bias,” a concept we define, and whose consequences for LLM-based research we further explore.
{"title":"Machine Bias. How Do Generative Language Models Answer Opinion Polls?","authors":"Julien Boelaert, Samuel Coavoux, Étienne Ollion, Ivaylo Petev, Patrick Präg","doi":"10.1177/00491241251330582","DOIUrl":"https://doi.org/10.1177/00491241251330582","url":null,"abstract":"Generative artificial intelligence (AI) is increasingly presented as a potential substitute for humans, including as research subjects. However, there is no scientific consensus on how closely these in silico clones can emulate survey respondents. While some defend the use of these “synthetic users,” others point toward social biases in the responses provided by large language models (LLMs). In this article, we demonstrate that these critics are right to be wary of using generative AI to emulate respondents, but probably not for the right reasons. Our results show (i) that to date, models cannot replace research subjects for opinion or attitudinal research; (ii) that they display a strong bias and a low variance on each topic; and (iii) that this bias randomly varies from one topic to the next. We label this pattern “machine bias,” a concept we define, and whose consequences for LLM-based research we further explore.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"37 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143853640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-19DOI: 10.1177/00491241251326819
Sandrine Chausson, Marion Fourcade, David J. Harding, Björn Ross, Grégory Renard
Modern computational text classification methods have brought social scientists tantalizingly close to the goal of unlocking vast insights buried in text data—from centuries of historical documents to streams of social media posts. Yet three barriers still stand in the way: the tedious labor of manual text annotation, the technical complexity that keeps these tools out of reach for many researchers, and, perhaps most critically, the challenge of bridging the gap between sophisticated algorithms and the deep theoretical understanding social scientists have already developed about human interactions, social structures, and institutions. To counter these limitations, we propose an approach to large-scale text analysis that requires substantially less human-labeled data, and no machine learning expertise, and efficiently integrates the social scientist into critical steps in the workflow. This approach, which allows the detection of statements in text, relies on large language models pre-trained for natural language inference, and a “few-shot” threshold-tuning algorithm rooted in active learning principles. We describe and showcase our approach by analyzing tweets collected during the 2020 U.S. presidential election campaign, and benchmark it against various computational approaches across three datasets.
{"title":"The Insight-Inference Loop: Efficient Text Classification via Natural Language Inference and Threshold-Tuning","authors":"Sandrine Chausson, Marion Fourcade, David J. Harding, Björn Ross, Grégory Renard","doi":"10.1177/00491241251326819","DOIUrl":"https://doi.org/10.1177/00491241251326819","url":null,"abstract":"Modern computational text classification methods have brought social scientists tantalizingly close to the goal of unlocking vast insights buried in text data—from centuries of historical documents to streams of social media posts. Yet three barriers still stand in the way: the tedious labor of manual text annotation, the technical complexity that keeps these tools out of reach for many researchers, and, perhaps most critically, the challenge of bridging the gap between sophisticated algorithms and the deep theoretical understanding social scientists have already developed about human interactions, social structures, and institutions. To counter these limitations, we propose an approach to large-scale text analysis that requires substantially less human-labeled data, and no machine learning expertise, and efficiently integrates the social scientist into critical steps in the workflow. This approach, which allows the detection of statements in text, relies on large language models pre-trained for natural language inference, and a “few-shot” threshold-tuning algorithm rooted in active learning principles. We describe and showcase our approach by analyzing tweets collected during the 2020 U.S. presidential election campaign, and benchmark it against various computational approaches across three datasets.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"1 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-19DOI: 10.1177/00491241251322517
Diego F. Leal
Although the literature on cultural holes has expanded considerably in recent years, there is no concrete measure in that literature to locate cultural holes brokers. This article develops a conceptual framework grounded in social network theory and cultural sociology to propose a specific solution to fill this measurement gap. Agent-based computational experiments are leveraged to develop a theoretical test of the analytic purchase and distinctiveness of the proposed measure, termed potential for intercultural brokerage (PIB). Results demonstrate the effectiveness of PIB in locating early adopters that can achieve widespread levels of diffusion in societies segregated along bright symbolic boundaries. Findings also show the superiority of PIB when compared to classic alternative measures in the network literature that focus on locating early adopters based on structural holes (e.g., network constraint, effective size), geodesics (e.g., betweenness centrality), and degree (e.g., degree centrality), among other classic network measures. Broader implications of these findings for brokerage theory are discussed herein.
{"title":"Locating Cultural Holes Brokers in Diffusion Dynamics Across Bright Symbolic Boundaries","authors":"Diego F. Leal","doi":"10.1177/00491241251322517","DOIUrl":"https://doi.org/10.1177/00491241251322517","url":null,"abstract":"Although the literature on cultural holes has expanded considerably in recent years, there is no concrete measure in that literature to locate cultural holes brokers. This article develops a conceptual framework grounded in social network theory and cultural sociology to propose a specific solution to fill this measurement gap. Agent-based computational experiments are leveraged to develop a theoretical test of the analytic purchase and distinctiveness of the proposed measure, termed potential for intercultural brokerage (PIB). Results demonstrate the effectiveness of PIB in locating early adopters that can achieve widespread levels of diffusion in societies segregated along bright symbolic boundaries. Findings also show the superiority of PIB when compared to classic alternative measures in the network literature that focus on locating early adopters based on structural holes (e.g., network constraint, effective size), geodesics (e.g., betweenness centrality), and degree (e.g., degree centrality), among other classic network measures. Broader implications of these findings for brokerage theory are discussed herein.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"19 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143661168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-12DOI: 10.1177/00491241251319291
Sourabh Balgi, Adel Daoud, Jose M. Peña, Geoffrey T. Wodtke, Jesse Zhou
Social science theories often postulate systems of causal relationships among variables, which are commonly represented using directed acyclic graphs (DAGs). As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify empirical evaluation, researchers typically invoke such assumptions anyway, even though they are often arbitrary and do not reflect any theoretical content or prior knowledge. Moreover, functional form assumptions can engender bias, whenever they fail to accurately capture the true complexity of the system. In this article, we introduce causal-graphical normalizing flows (cGNFs), a novel approach to causal inference that leverages deep neural networks to empirically evaluate theories represented as DAGs. Unlike conventional methods, cGNFs model the full joint distribution of the data using a DAG specified by the analyst, without relying on stringent assumptions about functional form. This enables flexible, non-parametric estimation of any causal estimand identified from the DAG, including total effects, direct and indirect effects, and path-specific effects. We illustrate the method with a reanalysis of Blau and Duncan’s ( 1967 ) model of status attainment and Zhou’s ( 2019 ) model of controlled mobility. The article concludes with a discussion of current limitations and directions for future development.
{"title":"Deep Learning With DAGs","authors":"Sourabh Balgi, Adel Daoud, Jose M. Peña, Geoffrey T. Wodtke, Jesse Zhou","doi":"10.1177/00491241251319291","DOIUrl":"https://doi.org/10.1177/00491241251319291","url":null,"abstract":"Social science theories often postulate systems of causal relationships among variables, which are commonly represented using directed acyclic graphs (DAGs). As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify empirical evaluation, researchers typically invoke such assumptions anyway, even though they are often arbitrary and do not reflect any theoretical content or prior knowledge. Moreover, functional form assumptions can engender bias, whenever they fail to accurately capture the true complexity of the system. In this article, we introduce causal-graphical normalizing flows (cGNFs), a novel approach to causal inference that leverages deep neural networks to empirically evaluate theories represented as DAGs. Unlike conventional methods, cGNFs model the full joint distribution of the data using a DAG specified by the analyst, without relying on stringent assumptions about functional form. This enables flexible, non-parametric estimation of any causal estimand identified from the DAG, including total effects, direct and indirect effects, and path-specific effects. We illustrate the method with a reanalysis of Blau and Duncan’s ( <jats:xref ref-type=\"bibr\">1967</jats:xref> ) model of status attainment and Zhou’s ( <jats:xref ref-type=\"bibr\">2019</jats:xref> ) model of controlled mobility. The article concludes with a discussion of current limitations and directions for future development.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"11 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143608045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-03DOI: 10.1177/00491241251314039
Tay Jeong
According to the interventionist framework of actual causality, causal claims in history are ultimately claims about special types of functional dependencies between variables, which consist not only of actual events but also of corresponding counterfactual states of affairs. Instead of advocating the methodological use of counterfactuals tout court, we propose specific circumstances in historical writing where counterfactual reasoning comes in most handy. At the level of semantics, that is, the specification of the variables and their possible values, an explicit specification of the latent contrast classes becomes particularly useful in situations where one may be prompted to take an event that is pre-empted by the antecedent of interest as its proper causal contrast. At the level of inference, we argue that cases in which two or more antecedents appear to be playing a similar role tend to fumble our pretheoretical intuition about cause and propose a sequence of counterfactual tests based on actual examples from causal historiography.
{"title":"When to Use Counterfactuals in Causal Historiography: Methods for Semantics and Inference","authors":"Tay Jeong","doi":"10.1177/00491241251314039","DOIUrl":"https://doi.org/10.1177/00491241251314039","url":null,"abstract":"According to the interventionist framework of actual causality, causal claims in history are ultimately claims about special types of functional dependencies between variables, which consist not only of actual events but also of corresponding counterfactual states of affairs. Instead of advocating the methodological use of counterfactuals tout court, we propose specific circumstances in historical writing where counterfactual reasoning comes in most handy. At the level of semantics, that is, the specification of the variables and their possible values, an explicit specification of the latent contrast classes becomes particularly useful in situations where one may be prompted to take an event that is pre-empted by the antecedent of interest as its proper causal contrast. At the level of inference, we argue that cases in which two or more antecedents appear to be playing a similar role tend to fumble our pretheoretical intuition about cause and propose a sequence of counterfactual tests based on actual examples from causal historiography.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"10 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143084171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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