Abstract:Instrumental variable (IV) strategies are widely used to estimate causal effects in economics, political science, epidemiology, sociology, psychology, and other fields. When there is unobserved heterogeneity in causal effects, standard linear IV estimators only represent effects for complier subpopulations (Imbens and Angrist, 1994). Marginal treatment effect (MTE) methods (Heckman and Vytlacil, 1999, 2005) allow researchers to use additional assumptions to extrapolate beyond complier subpopulations. We discuss a flexible framework for MTE methods based on linear regression and the generalized method of moments. We show how to implement the framework using the ivmte package for R.
{"title":"ivmte: An R Package for Extrapolating Instrumental Variable Estimates Away From Compliers*","authors":"Joshua Shea, Alexander Torgovitsky","doi":"10.1353/obs.2023.0016","DOIUrl":"https://doi.org/10.1353/obs.2023.0016","url":null,"abstract":"Abstract:Instrumental variable (IV) strategies are widely used to estimate causal effects in economics, political science, epidemiology, sociology, psychology, and other fields. When there is unobserved heterogeneity in causal effects, standard linear IV estimators only represent effects for complier subpopulations (Imbens and Angrist, 1994). Marginal treatment effect (MTE) methods (Heckman and Vytlacil, 1999, 2005) allow researchers to use additional assumptions to extrapolate beyond complier subpopulations. We discuss a flexible framework for MTE methods based on linear regression and the generalized method of moments. We show how to implement the framework using the ivmte package for R.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"1 - 42"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45602939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:In this commentary, I provide a personal perspective on how the propensity score has become important to epidemiology.
摘要:在这篇评论中，我从个人角度阐述了倾向评分对流行病学的重要性。
{"title":"The central role of the propensity score in epidemiology","authors":"Brian K. Lee","doi":"10.1353/obs.2023.0004","DOIUrl":"https://doi.org/10.1353/obs.2023.0004","url":null,"abstract":"Abstract:In this commentary, I provide a personal perspective on how the propensity score has become important to epidemiology.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"19 1","pages":"55 - 57"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66460632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:Rosenbaum and Rubin’s paper is highly cited because the basic idea is simple and insightful, and it has applications to important practical problems in treatment comparisons with observational data, and selection bias and nonresponse in surveys. I discuss several issues related to the method, including use of the propensity score for weighting or prediction, and two robust methods that use the propensity score as a covariate and can be more efficient that weighting when the weights are highly variable, namely Penalized Spline of Propensity Prediction (PSPP) and Penalized Spline of Propensity for Treatment Comparisons (PENCOMP). Approaches to addressing highly variable weights are discussed, including omitting variables in the propensity model that are unrelated to outcomes, and redefining the estimand.
{"title":"Some Reflections on Rosenbaum and Rubin’s Propensity Score Paper","authors":"R. Little","doi":"10.1353/obs.2023.0006","DOIUrl":"https://doi.org/10.1353/obs.2023.0006","url":null,"abstract":"Abstract:Rosenbaum and Rubin’s paper is highly cited because the basic idea is simple and insightful, and it has applications to important practical problems in treatment comparisons with observational data, and selection bias and nonresponse in surveys. I discuss several issues related to the method, including use of the propensity score for weighting or prediction, and two robust methods that use the propensity score as a covariate and can be more efficient that weighting when the weights are highly variable, namely Penalized Spline of Propensity Prediction (PSPP) and Penalized Spline of Propensity for Treatment Comparisons (PENCOMP). Approaches to addressing highly variable weights are discussed, including omitting variables in the propensity model that are unrelated to outcomes, and redefining the estimand.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"69 - 75"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49646766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:The foundational propensity score paper by Rosenbaum and Rubin (1983a) laid the foundation for a set of methods widely used in the design of non-experimental studies. This commentary reflects on the theoretical contributions of that paper –especially the idea of the propensity score as a balancing score –as well as on the wide variety of contexts in which the general idea of a balancing score has since been applied. Areas in which the fundamental ideas of a balancing score –which can help equate two groups on the basis of a set of covariates –have been extended include mediation analysis and generalizability. The commentary also touches on common misperceptions regarding propensity scores, and on the key role of the “other” Rosenbaum and Rubin (1983b) paper, which laid out a method for assessing the sensitivity of study results to violation of the key assumption underlying most uses of propensity scores –that of no unmeasured confounding. All together, this body of work has changed how many fields conduct non-experimental studies, and other related types of studies, and with many applications and extensions yet to come.
{"title":"What is a propensity score? Applications and extensions of balancing score methods","authors":"E. Stuart","doi":"10.1353/obs.2023.0011","DOIUrl":"https://doi.org/10.1353/obs.2023.0011","url":null,"abstract":"Abstract:The foundational propensity score paper by Rosenbaum and Rubin (1983a) laid the foundation for a set of methods widely used in the design of non-experimental studies. This commentary reflects on the theoretical contributions of that paper –especially the idea of the propensity score as a balancing score –as well as on the wide variety of contexts in which the general idea of a balancing score has since been applied. Areas in which the fundamental ideas of a balancing score –which can help equate two groups on the basis of a set of covariates –have been extended include mediation analysis and generalizability. The commentary also touches on common misperceptions regarding propensity scores, and on the key role of the “other” Rosenbaum and Rubin (1983b) paper, which laid out a method for assessing the sensitivity of study results to violation of the key assumption underlying most uses of propensity scores –that of no unmeasured confounding. All together, this body of work has changed how many fields conduct non-experimental studies, and other related types of studies, and with many applications and extensions yet to come.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"113 - 117"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45664986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:Rosenbaum and Rubin’s (1983) propensity score revolutionized the field of causal inference and has emerged as a standard tool when researchers reason about cause-and-effect relationship across many disciplines. This discussion centers around the key “no interference” assumption in Rosenbaum and Rubin’s original development of the propensity score and reviews some recent advances in extending the propensity score to studies involving dependent happenings.
{"title":"Propensity Score in the Face of Interference: Discussion of Rosenbaum and Rubin (1983)","authors":"Bo Zhang, M. Hudgens, M. Halloran","doi":"10.1353/obs.2023.0013","DOIUrl":"https://doi.org/10.1353/obs.2023.0013","url":null,"abstract":"Abstract:Rosenbaum and Rubin’s (1983) propensity score revolutionized the field of causal inference and has emerged as a standard tool when researchers reason about cause-and-effect relationship across many disciplines. This discussion centers around the key “no interference” assumption in Rosenbaum and Rubin’s original development of the propensity score and reviews some recent advances in extending the propensity score to studies involving dependent happenings.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"125 - 131"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48628021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:Rosenbaum and Rubin (1983) suggested a visual representation, that can be used as a diagnostic tool, for examining whether the relationships between confounders and outcomes are sufficiently controlled, or whether there is a more complex relationship that requires further adjustment. This short commentary highlights this simple tool, providing an example of its utility along with relevant R code.
{"title":"A Visual Diagnostic Tool for Causal Inference","authors":"Lucy D’Agostino McGowan, Ralph B. D’Agostino","doi":"10.1353/obs.2023.0008","DOIUrl":"https://doi.org/10.1353/obs.2023.0008","url":null,"abstract":"Abstract:Rosenbaum and Rubin (1983) suggested a visual representation, that can be used as a diagnostic tool, for examining whether the relationships between confounders and outcomes are sufficiently controlled, or whether there is a more complex relationship that requires further adjustment. This short commentary highlights this simple tool, providing an example of its utility along with relevant R code.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"87 - 95"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46392615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:Rubin and Rosenbaum (1983) wrote about the theory and application of “propensity scores” in their landmark paper. Since that time, the method has still been in use or adapted for use in various contexts. In this commentary, I discuss their original paper and the latest in terms of criticisms and defense of the use of some of the theory they proposed for propensity score matching. Although the commentary is not exhaustive, I try to highlight important aspects of their theory as well as points made later for and against some of their originally proposed theory.
{"title":"Commentary on Rubin and Rosenbaum Seminal 1983 Paper on Propensity Scores: From Then to Now","authors":"Usha Govindarajulu","doi":"10.1353/obs.2023.0000","DOIUrl":"https://doi.org/10.1353/obs.2023.0000","url":null,"abstract":"Abstract:Rubin and Rosenbaum (1983) wrote about the theory and application of “propensity scores” in their landmark paper. Since that time, the method has still been in use or adapted for use in various contexts. In this commentary, I discuss their original paper and the latest in terms of criticisms and defense of the use of some of the theory they proposed for propensity score matching. Although the commentary is not exhaustive, I try to highlight important aspects of their theory as well as points made later for and against some of their originally proposed theory.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"19 - 22"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46521470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:Rosenbaum and Rubin’s seminal work on the propensity score set the stage for decades of subsequent developments in causal inference methodology for use in observational studies. In this commentary, I discuss two specific aspects of their work with particular emphasis on how they have shaped my understanding of causal inference: (1) the propensity score as a data reduction technique, and (2) the importance of drawing parallels between the observational study and the randomized experiment.
{"title":"The Central Role of Rosenbaum and Rubin’s Seminal Work","authors":"A. Spieker","doi":"10.1353/obs.2023.0010","DOIUrl":"https://doi.org/10.1353/obs.2023.0010","url":null,"abstract":"Abstract:Rosenbaum and Rubin’s seminal work on the propensity score set the stage for decades of subsequent developments in causal inference methodology for use in observational studies. In this commentary, I discuss two specific aspects of their work with particular emphasis on how they have shaped my understanding of causal inference: (1) the propensity score as a data reduction technique, and (2) the importance of drawing parallels between the observational study and the randomized experiment.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"105 - 112"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48592462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract:The propensity score, which was originally introduced in Rosenbaum and Rubin (1983), has been widely considered one of the most important concepts in the causal inference literature. This article briefly reviews some propensity score models involving both observed and unobserved covariates and discusses their applications in sensitivity analysis for matched observational studies.
{"title":"The Central Role of the Propensity Score in Sensitivity Analysis for Matched Observational Studies","authors":"Siyu Heng","doi":"10.1353/obs.2023.0002","DOIUrl":"https://doi.org/10.1353/obs.2023.0002","url":null,"abstract":"Abstract:The propensity score, which was originally introduced in Rosenbaum and Rubin (1983), has been widely considered one of the most important concepts in the causal inference literature. This article briefly reviews some propensity score models involving both observed and unobserved covariates and discusses their applications in sensitivity analysis for matched observational studies.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"35 - 41"},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45075047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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