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}
Pub Date : 2025-01-23DOI: 10.1177/00491241241295336
Lion Behrens, Ingo Rohlfing
In this article, we develop a mixed-methods design that combines Bayesian regression with Bayesian process tracing. A fully Bayesian multimethod design allows one to include empirical knowledge at each stage of the analysis and to coherently transfer information from the quantitative to the qualitative analysis, and vice versa. We present a complete mixed-methods workflow explaining how this is accomplished and how to integrate both methods. It is demonstrated how to use the posterior highest density interval and the Bayes factor from the regression analysis to update the prior level of confidence about what mechanisms possibly connect the cause to the outcome. It is further shown how to choose cases for the qualitative analysis through posterior predictive sampling. We illustrate this approach with an empirical analysis of colonial development and compare it with alternative designs, including nested analysis and the Bayesian integration of qualitative and quantitative methods.
{"title":"The Integration of Bayesian Regression Analysis and Bayesian Process Tracing in Mixed-Methods Research","authors":"Lion Behrens, Ingo Rohlfing","doi":"10.1177/00491241241295336","DOIUrl":"https://doi.org/10.1177/00491241241295336","url":null,"abstract":"In this article, we develop a mixed-methods design that combines Bayesian regression with Bayesian process tracing. A fully Bayesian multimethod design allows one to include empirical knowledge at each stage of the analysis and to coherently transfer information from the quantitative to the qualitative analysis, and vice versa. We present a complete mixed-methods workflow explaining how this is accomplished and how to integrate both methods. It is demonstrated how to use the posterior highest density interval and the Bayes factor from the regression analysis to update the prior level of confidence about what mechanisms possibly connect the cause to the outcome. It is further shown how to choose cases for the qualitative analysis through posterior predictive sampling. We illustrate this approach with an empirical analysis of colonial development and compare it with alternative designs, including nested analysis and the Bayesian integration of qualitative and quantitative methods.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"12 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143026628","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 : 2025-01-21DOI: 10.1177/00491241241307600
Natalja Menold, Patricia Hadler, Cornelia Neuert
The study addresses the effects of piloting methods on the cross-cultural comparability and reliability of the measurement of gender and age stereotypes. We conducted a summative evaluation of expert reviews, cognitive pretests and web probing. We first piloted a gender role, an ageism, and a children stereotypes instrument in German and American English. We then randomly assigned the original and piloted versions to respondents in Germany and the United States using an online survey experiment and quota samples. No configural invariance was shown by the original instruments and the reliability of the gender role instrument was insufficiently low. The results show that piloting methods increased reliability and improved measurement invariance, although the effects varied by topic. Cross-cultural expert reviews and web probing provided more consistent results than other methods. A combination of web probing and cross-cultural expert reviews can maximize both reliability and measurement invariance.
{"title":"Improving Cross-Cultural Comparability of Measures on Gender and Age Stereotypes by Means of Piloting Methods","authors":"Natalja Menold, Patricia Hadler, Cornelia Neuert","doi":"10.1177/00491241241307600","DOIUrl":"https://doi.org/10.1177/00491241241307600","url":null,"abstract":"The study addresses the effects of piloting methods on the cross-cultural comparability and reliability of the measurement of gender and age stereotypes. We conducted a summative evaluation of expert reviews, cognitive pretests and web probing. We first piloted a gender role, an ageism, and a children stereotypes instrument in German and American English. We then randomly assigned the original and piloted versions to respondents in Germany and the United States using an online survey experiment and quota samples. No configural invariance was shown by the original instruments and the reliability of the gender role instrument was insufficiently low. The results show that piloting methods increased reliability and improved measurement invariance, although the effects varied by topic. Cross-cultural expert reviews and web probing provided more consistent results than other methods. A combination of web probing and cross-cultural expert reviews can maximize both reliability and measurement invariance.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"105 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991933","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 : 2025-01-16DOI: 10.1177/00491241241303517
Andrew Taeho Kim, ChangHwan Kim
Occupation is a construct prone to classification mismatches by coders and description inconsistency by respondents. We explore whether mismatches in occupational coding have recently increased, what factors are associated with the rise in mismatches, and how the rise affects estimates of intragenerational occupational mobility. Utilizing the 1991–2020 Annual Social and Economic Supplement of the Current Population Survey, which collects information on respondents’ current occupation and the previous year’s main occupation, we identify coding mismatches and compare the probabilities of occupational mobility based on four combinations of two variables. Our results show that not only do the estimates of occupational mobility between two adjacent years vary substantially across measures, but also that the magnitudes of intragenerational occupational mobility across measures become increasingly decoupled over time. We demonstrate that the likely cause of this divergence is the rise in coding mismatches between coders. We discuss the implications of our findings.
{"title":"The Rise in Occupational Coding Mismatches and Occupational Mobility, 1991–2020","authors":"Andrew Taeho Kim, ChangHwan Kim","doi":"10.1177/00491241241303517","DOIUrl":"https://doi.org/10.1177/00491241241303517","url":null,"abstract":"Occupation is a construct prone to classification mismatches by coders and description inconsistency by respondents. We explore whether mismatches in occupational coding have recently increased, what factors are associated with the rise in mismatches, and how the rise affects estimates of intragenerational occupational mobility. Utilizing the 1991–2020 Annual Social and Economic Supplement of the Current Population Survey, which collects information on respondents’ current occupation and the previous year’s main occupation, we identify coding mismatches and compare the probabilities of occupational mobility based on four combinations of two variables. Our results show that not only do the estimates of occupational mobility between two adjacent years vary substantially across measures, but also that the magnitudes of intragenerational occupational mobility across measures become increasingly decoupled over time. We demonstrate that the likely cause of this divergence is the rise in coding mismatches between coders. We discuss the implications of our findings.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"30 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986720","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 : 2025-01-16DOI: 10.1177/00491241241305095
Brad R Fulton, David P King
Organizational researchers use a variety of methods to obtain sampling frames. The utility of these methods, however, is constrained by access restrictions, limited coverage, prohibitive costs, and cumbersome formats. This article presents a new method for generating organizational sampling frames that is cost-effective, uses publicly available data, and can produce sampling frames for many geographic areas in the U.S. The Python-based program we developed systematically scans the Google Maps platform to identify organizations of interest and retrieve their contact information. We demonstrate the program's viability and utility by generating a sampling frame of religious congregations in the U.S. To assess Google Maps’ coverage and representativeness of such congregations, we examined two nationally representative samples of congregations and censuses of congregations in a small, medium, and large city. We found that Google Maps contains approximately 98% of those congregations––extensive coverage that ensures a high degree of representativeness. This study provides evidence that using Google Maps to generate sampling frames can improve the process for obtaining representative samples for organizational studies by reducing costs, increasing efficiency, and providing greater coverage and representativeness.
{"title":"Using Google Maps to Generate Organizational Sampling Frames","authors":"Brad R Fulton, David P King","doi":"10.1177/00491241241305095","DOIUrl":"https://doi.org/10.1177/00491241241305095","url":null,"abstract":"Organizational researchers use a variety of methods to obtain sampling frames. The utility of these methods, however, is constrained by access restrictions, limited coverage, prohibitive costs, and cumbersome formats. This article presents a new method for generating organizational sampling frames that is cost-effective, uses publicly available data, and can produce sampling frames for many geographic areas in the U.S. The Python-based program we developed systematically scans the Google Maps platform to identify organizations of interest and retrieve their contact information. We demonstrate the program's viability and utility by generating a sampling frame of religious congregations in the U.S. To assess Google Maps’ coverage and representativeness of such congregations, we examined two nationally representative samples of congregations and censuses of congregations in a small, medium, and large city. We found that Google Maps contains approximately 98% of those congregations––extensive coverage that ensures a high degree of representativeness. This study provides evidence that using Google Maps to generate sampling frames can improve the process for obtaining representative samples for organizational studies by reducing costs, increasing efficiency, and providing greater coverage and representativeness.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"38 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142986727","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 : 2024-12-05DOI: 10.1177/00491241241298914
Kristen Olson, John Stevenson, Nadia Assad, Lindsey Witt-Swanson, Cameron P.E. Jones, Amanda Ganshert, Jennifer Dykema
Self-administered surveys may be administered with a single mode or mixed data collection modes. How mixing modes of data collection affects survey costs is not well understood. We examine whether cost structures differ for mail-only versus web+mail mixed-mode surveys, what design features are associated with costs, and whether survey costs are associated with response rates. Using administrative survey cost data from two academic survey centers, we find that survey costs per sampled unit and per complete vary substantially across individual surveys. The average cost per sampled unit is surprisingly similar across mail-only and web+mail surveys. How the budget is allocated across printing, postage, incentive, and staff time varies across these designs: printing and postage costs are higher in mail-only surveys, and more of the budget is allocated to incentive costs and project management costs in web+mail surveys. Furthermore, higher cost surveys are associated with higher response rates, particularly for incentive costs.
{"title":"Examining Variation in Survey Costs Across Surveys","authors":"Kristen Olson, John Stevenson, Nadia Assad, Lindsey Witt-Swanson, Cameron P.E. Jones, Amanda Ganshert, Jennifer Dykema","doi":"10.1177/00491241241298914","DOIUrl":"https://doi.org/10.1177/00491241241298914","url":null,"abstract":"Self-administered surveys may be administered with a single mode or mixed data collection modes. How mixing modes of data collection affects survey costs is not well understood. We examine whether cost structures differ for mail-only versus web+mail mixed-mode surveys, what design features are associated with costs, and whether survey costs are associated with response rates. Using administrative survey cost data from two academic survey centers, we find that survey costs per sampled unit and per complete vary substantially across individual surveys. The average cost per sampled unit is surprisingly similar across mail-only and web+mail surveys. How the budget is allocated across printing, postage, incentive, and staff time varies across these designs: printing and postage costs are higher in mail-only surveys, and more of the budget is allocated to incentive costs and project management costs in web+mail surveys. Furthermore, higher cost surveys are associated with higher response rates, particularly for incentive costs.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"1 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789908","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 : 2024-09-19DOI: 10.1177/00491241241277524
Joanne W. Golann, Lori Bougher, Richard Hall, Thomas J. Espenshade
Data sharing and transparency are becoming more common across the social sciences. In this article, we provide an overview of ethical, methodological, and technological considerations and challenges when developing large video-based datasets intended to be shared across researchers. We cover data security, storage, and access as well as data documentation, tagging, and transcription. Our discussions are framed by our own efforts to create a secure and user-friendly database for the New Jersey Families Study, a two-week, in-home video study of 21 families with a 2- to 4-year-old child. In collecting over 11,470 hours of video data, the New Jersey Families Study is one of the very few large-scale video projects in the field of sociology. This project has provided us with a unique opportunity to explore video data management and data sharing techniques, particularly in light of a host of cutting-edge developments in data science.
数据共享和透明度在社会科学领域越来越普遍。在本文中，我们将概述在开发大型视频数据集以供研究人员共享时，在伦理、方法和技术方面需要考虑的问题和面临的挑战。我们将讨论数据安全、存储和访问以及数据记录、标记和转录等问题。我们的讨论以我们自己为新泽西家庭研究（New Jersey Families Study）创建一个安全且用户友好的数据库所做的努力为框架，该研究是对 21 个有一个 2-4 岁孩子的家庭进行的为期两周的家庭视频研究。新泽西家庭研究收集了超过 11,470 小时的视频数据，是社会学领域为数不多的大型视频项目之一。该项目为我们提供了一个探索视频数据管理和数据共享技术的难得机会，尤其是在数据科学取得一系列前沿发展的情况下。
{"title":"Sharing Big Video Data: Ethics, Methods, and Technology","authors":"Joanne W. Golann, Lori Bougher, Richard Hall, Thomas J. Espenshade","doi":"10.1177/00491241241277524","DOIUrl":"https://doi.org/10.1177/00491241241277524","url":null,"abstract":"Data sharing and transparency are becoming more common across the social sciences. In this article, we provide an overview of ethical, methodological, and technological considerations and challenges when developing large video-based datasets intended to be shared across researchers. We cover data security, storage, and access as well as data documentation, tagging, and transcription. Our discussions are framed by our own efforts to create a secure and user-friendly database for the New Jersey Families Study, a two-week, in-home video study of 21 families with a 2- to 4-year-old child. In collecting over 11,470 hours of video data, the New Jersey Families Study is one of the very few large-scale video projects in the field of sociology. This project has provided us with a unique opportunity to explore video data management and data sharing techniques, particularly in light of a host of cutting-edge developments in data science.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"31 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142306403","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 : 2024-09-03DOI: 10.1177/00491241241268775
Tianyu Shen, Collin F. Payne, Maria Jahromi
Many studies have compared individual measures of health expectancy across older populations by time-invariant characteristics. However, very few have included time-varying variables when calculating health expectancy. Even among older adults, socioeconomic and demographic characteristics are likely to change over the life course, and these changes may have substantial implications for health outcomes. This paper proposes a multiple multistate method (MMM) that situates the multistate model within the broader family of vector autoregressive models. Our approach allows the incorporation of the coevolution of multiple life course factors and provides a flexible yet simple way to model two or more time-varying variables with the multistate model. We demonstrate the MMM in two empirical applications, showing the flexibility of the approach to explore health expectancies with complex state spaces.
{"title":"Dynamics of Health Expectancy: An Introduction to the Multiple Multistate Method (MMM)","authors":"Tianyu Shen, Collin F. Payne, Maria Jahromi","doi":"10.1177/00491241241268775","DOIUrl":"https://doi.org/10.1177/00491241241268775","url":null,"abstract":"Many studies have compared individual measures of health expectancy across older populations by time-invariant characteristics. However, very few have included time-varying variables when calculating health expectancy. Even among older adults, socioeconomic and demographic characteristics are likely to change over the life course, and these changes may have substantial implications for health outcomes. This paper proposes a multiple multistate method (MMM) that situates the multistate model within the broader family of vector autoregressive models. Our approach allows the incorporation of the coevolution of multiple life course factors and provides a flexible yet simple way to model two or more time-varying variables with the multistate model. We demonstrate the MMM in two empirical applications, showing the flexibility of the approach to explore health expectancies with complex state spaces.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"8 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130631","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 : 2024-08-22DOI: 10.1177/00491241241268453
Miriam Hurtado Bodell, Måns Magnusson, Marc Keuschnigg
Sociologists are discussing the need for more formal ways to extract meaning from digital text archives. We focus attention on the seeded topic model, a semi-supervised extension to the standard topic model that allows sociological knowledge to be infused into the computational learning of meaning structures. Seed words help crystallize topics around known concepts, while utilizing topic models’ functionality to identify associations in text based on word co-occurrences. The method estimates a concept’s shared interpretation (or framing) via its associations with other frequently co-occurring topics. In a case study, we extract longitudinal measures of media frames regarding immigration from a vast corpus of millions of Swedish newspaper articles from the period 1945–2019. We infer turning points that partition the immigration discourse into meaningful eras and locate Sweden’s era of multicultural ideals that coined its tolerant reputation.
{"title":"Seeded Topic Models in Digital Archives: Analyzing Interpretations of Immigration in Swedish Newspapers, 1945–2019","authors":"Miriam Hurtado Bodell, Måns Magnusson, Marc Keuschnigg","doi":"10.1177/00491241241268453","DOIUrl":"https://doi.org/10.1177/00491241241268453","url":null,"abstract":"Sociologists are discussing the need for more formal ways to extract meaning from digital text archives. We focus attention on the seeded topic model, a semi-supervised extension to the standard topic model that allows sociological knowledge to be infused into the computational learning of meaning structures. Seed words help crystallize topics around known concepts, while utilizing topic models’ functionality to identify associations in text based on word co-occurrences. The method estimates a concept’s shared interpretation (or framing) via its associations with other frequently co-occurring topics. In a case study, we extract longitudinal measures of media frames regarding immigration from a vast corpus of millions of Swedish newspaper articles from the period 1945–2019. We infer turning points that partition the immigration discourse into meaningful eras and locate Sweden’s era of multicultural ideals that coined its tolerant reputation.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"17 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142042539","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 : 2024-08-16DOI: 10.1177/00491241241234866
Bernard J. Koch, Tim Sainburg, Pablo Geraldo Bastías, Song Jiang, Yizhou Sun, Jacob G. Foster
This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction to building and optimizing custom deep learning models and shows how to adapt them to estimate/predict heterogeneous treatment effects. It also discusses ongoing work to extend causal inference to settings where confounding is nonlinear, time-varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The primer differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in TensorFlow 2 and PyTorch.
{"title":"A Primer on Deep Learning for Causal Inference","authors":"Bernard J. Koch, Tim Sainburg, Pablo Geraldo Bastías, Song Jiang, Yizhou Sun, Jacob G. Foster","doi":"10.1177/00491241241234866","DOIUrl":"https://doi.org/10.1177/00491241241234866","url":null,"abstract":"This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction to building and optimizing custom deep learning models and shows how to adapt them to estimate/predict heterogeneous treatment effects. It also discusses ongoing work to extend causal inference to settings where confounding is nonlinear, time-varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The primer differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in TensorFlow 2 and PyTorch.","PeriodicalId":21849,"journal":{"name":"Sociological Methods & Research","volume":"95 1","pages":""},"PeriodicalIF":6.3,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141994354","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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