[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118893-en":3,"doc-seo-118893-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118893,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Recent Developments in Causal Inference and Machine Learning - Review","Reviewing advances in causal inference relevant to sociology, this paper focuses on four themes: identifying and estimating causal effects, handling effect heterogeneity, addressing causal mediation, and studying temporal and spatial interference. It explains how machine learning—used primarily as an estimation strategy—can be combined with causal inference to mitigate bias while supporting more principled analysis of causal mechanisms in settings shaped by historical and life-cycle variation, social contexts, and networks. The review emphasizes how uncovering heterogeneity improves extrapolation and external validity and encourages sociologists to apply these approaches in empirical research.","UCLA  \nUCLA Previously Published Works  \nTitle  \nRecent Developments in Causal Inference and Machine Learning  \nPermalink  \n[https://escholarship.org/uc/item/0tg4t8bd](https://escholarship.org/uc/item/0tg4t8bd)  \nJournal  \nAnnual Review of Sociology, 49(1)  \nISSN  \n0360-0572  \nAuthors  \nBrand, Jennie E Zhou, Xiang Xie, Yu  \nPublication Date  \n2023-07-31  \nDOI  \n10.1146/annurev-soc-030420-015345  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nRecent Developments in Causal Inference and  \nMachine Learning  \nJennie E. Brand 1 Xiang Zhou2 Yu Xie3  \nDecember 2 , 2022  \nIn preparation for Annual Review of Sociology  \nWord count = 10,587 Abstract word count = 154  \n1 Professor of Sociology and Statistics, UCLA, Director, California Center for Population Research, CoDirector, Center for Social Statistics, [brand@soc.ucla.edu](brand@soc.ucla.edu).  \n2 Associate Professor of Sociology, Harvard University, [xiang_zhou@fas.harvard.edu](xiang_zhou@fas.harvard.edu).  \n3 Bert G. Kerstetter ’66 University Professor of Sociology, Princeton University, [yuxie@princeton.edu](yuxie@princeton.edu).  \nAbstract  \nThis paper provides a review of recent advances in causal inference relevant to sociology. We focus on a selective subset of contributions aligning with four broad topics: causal effect identification and estimation in general, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference. We describe how machine learning, as an estimation strategy, can be effectively combined with causal inference, which has been traditionally concerned with identification. The incorporation of machine learning in causal inference enables researchers to better address potential biases in estimating causal effects and uncover heterogeneous causal effects. Uncovering sources of effect heterogeneity is key for generalizing to populations beyond those under study. While sociology has long emphasized the importance of causal mechanisms, historical and life-cycle variation, and social contexts involving network interactions, recent conceptual and computational advances facilitate more principled estimation of causal effects under these settings. We encourage sociologists to incorporate these insights into their empirical research.  \nKeywords: causal inference; counterfactuals; machine learning; treatment effect heterogeneity; mediation; extrapolation; external validity;  \nRecent Developments in Causal Inference and Machine Learning  \n1 Introduction  \nMany important questions in the social sciences, and everyday life, are causal questions. For example, we want to know how parental divorce affects children, how attending college affects job prospects, or how moving to a new neighborhood affects children’s academic performance. We ask what would happen if individuals did or did not experience an event, like divorcing or attending college. Since reviews in sociology by Winship and Morgan (1999) and Gangl (2010) , the literature on causal inference has developed several new promising directions. Some of the most exciting areas of development lie at the intersection of causal inference with machine learning (Athey & Imbens 2017, 2019; Huber 2021) . This review describes several key identification strategies for causal inference and how machine learning methods can enhance our estimation of causal effects. Throughout our review, we describe some empirical applications of these methods in sociology.4  \nWe emphasize four main principles in our review. First, the plausibility of the assumptions underlying different research designs and identification strategies varies by applications. Machine learning methods adapted to causal tasks facilitate estimation, but like other estimation tools, they do not assure identification of causal effects. Second, causal effect heterogeneity is the norm, and it complicates extrapolation. Researchers may exert considerable effort in ","cbCaid8fcHCqb0Or","https://ap.wps.com/l/cbCaid8fcHCqb0Or","pdf",687369,1,63,"English","en",105,"# Introduction\n## Causal questions in social science\n## Four guiding principles\n# Causal Effect Identification and Estimation\n## Notation and estimands\n# Causal Effect Heterogeneity\n# Causal Effect Mediation\n# Temporal and Spatial Interference\n# Conclusion","[{\"question\":\"What four topics does the paper focus on in causal inference for sociology?\",\"answer\":\"It focuses on causal effect identification and estimation, causal effect heterogeneity, causal effect mediation, and temporal and spatial interference.\"},{\"question\":\"How does the paper connect machine learning with causal inference?\",\"answer\":\"Machine learning is presented as an estimation strategy that can complement causal inference, helping researchers address potential biases and study heterogeneous causal effects.\"},{\"question\":\"Why is causal effect heterogeneity important for generalizing results?\",\"answer\":\"Heterogeneity is described as the norm and a key challenge for extrapolation, so identifying subpopulations most responsive to treatments supports external validity beyond the study population.\"}]","Recent Developments in Causal Inference and Machine Learning - 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