{"title":"Perspective on Interviews with Heckman, Pearl, Robins and Rubin","authors":"V. Didelez","doi":"10.1353/obs.2022.0010","DOIUrl":"https://doi.org/10.1353/obs.2022.0010","url":null,"abstract":"","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"8 1","pages":"104 - 95"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47605672","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}
{"title":"Interview with James Heckman","authors":"J. Heckman","doi":"10.1353/obs.2022.0006","DOIUrl":"https://doi.org/10.1353/obs.2022.0006","url":null,"abstract":"","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"8 1","pages":"22 - 7"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42601495","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:Propensity score plays a central role in causal inference, but its use is not limited to causal comparisons. As a covariate balancing tool, propensity score can be used for controlled descriptive comparisons between groups whose memberships are not manipulable. A prominent example is racial disparities in health care. However, conceptual confusion and hesitation persists for using propensity score in racial disparities studies. In this commentary, we argue that propensity score, possibly combined with other methods, is an effective tool for racial disparities analysis. We describe relevant estimands, target population, and assumptions. In particular, we clarify that a controlled descriptive comparison requires weaker assumptions than a causal comparison. We discuss three common propensity score weighting strategies: overlap weighting, inverse probability weighting and average treatment effect for treated weighting. We further describe how to combine weighting with the rank-and-replace adjustment method to produce racial disparity estimates concordant to the Institute of Medicine’s definition. The method is illustrated by a re-analysis of the Medical Expenditure Panel Survey data.
{"title":"Using propensity scores for racial disparities analysis","authors":"Fan Li","doi":"10.1353/obs.2023.0005","DOIUrl":"https://doi.org/10.1353/obs.2023.0005","url":null,"abstract":"Abstract:Propensity score plays a central role in causal inference, but its use is not limited to causal comparisons. As a covariate balancing tool, propensity score can be used for controlled descriptive comparisons between groups whose memberships are not manipulable. A prominent example is racial disparities in health care. However, conceptual confusion and hesitation persists for using propensity score in racial disparities studies. In this commentary, we argue that propensity score, possibly combined with other methods, is an effective tool for racial disparities analysis. We describe relevant estimands, target population, and assumptions. In particular, we clarify that a controlled descriptive comparison requires weaker assumptions than a causal comparison. We discuss three common propensity score weighting strategies: overlap weighting, inverse probability weighting and average treatment effect for treated weighting. We further describe how to combine weighting with the rank-and-replace adjustment method to produce racial disparity estimates concordant to the Institute of Medicine’s definition. The method is illustrated by a re-analysis of the Medical Expenditure Panel Survey data.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"59 - 68"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45405608","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:About forty years ago, in a now–seminal contribution, Rosenbaum and Rubin (1983) introduced a critical characterization of the propensity score as a central quantity for drawing causal inferences in observational study settings. In the decades since, much progress has been made across several research frontiers in causal inference, notably including the re-weighting and matching paradigms. Focusing on the former and specifically on its intersection with machine learning and semiparametric efficiency theory, we re-examine the role of the propensity score in modern methodological developments. As Rosenbaum and Rubin (1983)’s contribution spurred a focus on the balancing property of the propensity score, we re-examine the degree to which and how this property plays a role in the development of asymptotically efficient estimators of causal effects; moreover, we discuss a connection between the balancing property and efficient estimation in the form of score equations and propose a score test for evaluating whether an estimator achieves empirical balance.
{"title":"Revisiting the Propensity Score’s Central Role: Towards Bridging Balance and Efficiency in the Era of Causal Machine Learning","authors":"N. Hejazi, M. J. van der Laan","doi":"10.1353/obs.2023.0001","DOIUrl":"https://doi.org/10.1353/obs.2023.0001","url":null,"abstract":"Abstract:About forty years ago, in a now–seminal contribution, Rosenbaum and Rubin (1983) introduced a critical characterization of the propensity score as a central quantity for drawing causal inferences in observational study settings. In the decades since, much progress has been made across several research frontiers in causal inference, notably including the re-weighting and matching paradigms. Focusing on the former and specifically on its intersection with machine learning and semiparametric efficiency theory, we re-examine the role of the propensity score in modern methodological developments. As Rosenbaum and Rubin (1983)’s contribution spurred a focus on the balancing property of the propensity score, we re-examine the degree to which and how this property plays a role in the development of asymptotically efficient estimators of causal effects; moreover, we discuss a connection between the balancing property and efficient estimation in the form of score equations and propose a score test for evaluating whether an estimator achieves empirical balance.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"23 - 34"},"PeriodicalIF":0.0,"publicationDate":"2022-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48027197","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 paper presents some models for the propensity score. Considerable attention is given to a recently popular, but relatively under-explored setting in causal inference where the no-interference assumption does not hold. We lay out some key challenges in propensity score modeling under interference and present a few promising models based on existing works on mixed effects models.
{"title":"Propensity Score Modeling: Key Challenges When Moving Beyond the No-Interference Assumption","authors":"Hyunseung Kang, Chan Park, R. Trane","doi":"10.1353/obs.2023.0003","DOIUrl":"https://doi.org/10.1353/obs.2023.0003","url":null,"abstract":"Abstract:The paper presents some models for the propensity score. Considerable attention is given to a recently popular, but relatively under-explored setting in causal inference where the no-interference assumption does not hold. We lay out some key challenges in propensity score modeling under interference and present a few promising models based on existing works on mixed effects models.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"9 1","pages":"43 - 53"},"PeriodicalIF":0.0,"publicationDate":"2022-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47342181","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 Mann-Whitney test is a popular nonparametric test for comparing two samples. It has been recently extended by Satten et al. (2018) to allow testing for the existence of treatment effects in observational studies. Their proposed adjusted Mann-Whitney test relies on the unconfoundedness assumption which is untestable in practice. It hence becomes important to assess the impact of violating this assumption on the degree to which causal conclusions remain valid. In this paper, we consider a marginal sensitivity analysis framework to address this problem by utilizing a bootstrap approach that provides a sensitivity interval for the estimand with a guaranteed coverage probability as long as the data generating mechanism is included in the set of pre-specified sensitivity models. We develop efficient optimization algorithms for computing the sensitivity interval and further extend our approach to a general class of adjusted multi-sample U-statistics. Simulation studies and two real data applications are discussed to demonstrate the utility of our proposed methodology.
{"title":"Sensitivity Analysis for the Adjusted Mann-Whitney Test with Observational Studies","authors":"Maozhu Dai, Weining Shen, H. Stern","doi":"10.1353/obs.2022.0002","DOIUrl":"https://doi.org/10.1353/obs.2022.0002","url":null,"abstract":"Abstract:The Mann-Whitney test is a popular nonparametric test for comparing two samples. It has been recently extended by Satten et al. (2018) to allow testing for the existence of treatment effects in observational studies. Their proposed adjusted Mann-Whitney test relies on the unconfoundedness assumption which is untestable in practice. It hence becomes important to assess the impact of violating this assumption on the degree to which causal conclusions remain valid. In this paper, we consider a marginal sensitivity analysis framework to address this problem by utilizing a bootstrap approach that provides a sensitivity interval for the estimand with a guaranteed coverage probability as long as the data generating mechanism is included in the set of pre-specified sensitivity models. We develop efficient optimization algorithms for computing the sensitivity interval and further extend our approach to a general class of adjusted multi-sample U-statistics. Simulation studies and two real data applications are discussed to demonstrate the utility of our proposed methodology.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"8 1","pages":"1 - 29"},"PeriodicalIF":0.0,"publicationDate":"2022-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46940922","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}
Michela Bia, Alfonso Flores-Lagunes, Andrea Mercatanti
Abstract:In a world increasingly globalized, multiple language skills can create more employment opportunities. Several countries include language training programs in active labor market programs for the unemployed. We analyze the effects of a language training program on the re-employment probability and hourly wages simultaneously, using high-quality administrative data from Luxembourg. We address selection into training with an unconfoundedness assumption and account for the complication that wages are “truncated” by unemployment by adopting a principal stratification framework. Estimation is undertaken with a mixture model likelihood-based approach. To improve inference, we use the individual’s hours worked as a secondary outcome and a stochastic dominance assumption. These two features considerably ameliorate the multimodality problem commonly encountered in mixture models. We also conduct a sensitivity analysis to assess the unconfoundedness assumption. Our results suggest a positive effect (of up to 12.7 percent) of the language training programs on the re-employment probability, but no effects on wages for those who are observed employed regardless of training participation.
{"title":"Evaluation of Language Training Programs in Luxembourg using Principal Stratification","authors":"Michela Bia, Alfonso Flores-Lagunes, Andrea Mercatanti","doi":"10.2139/ssrn.3538309","DOIUrl":"https://doi.org/10.2139/ssrn.3538309","url":null,"abstract":"Abstract:In a world increasingly globalized, multiple language skills can create more employment opportunities. Several countries include language training programs in active labor market programs for the unemployed. We analyze the effects of a language training program on the re-employment probability and hourly wages simultaneously, using high-quality administrative data from Luxembourg. We address selection into training with an unconfoundedness assumption and account for the complication that wages are “truncated” by unemployment by adopting a principal stratification framework. Estimation is undertaken with a mixture model likelihood-based approach. To improve inference, we use the individual’s hours worked as a secondary outcome and a stochastic dominance assumption. These two features considerably ameliorate the multimodality problem commonly encountered in mixture models. We also conduct a sensitivity analysis to assess the unconfoundedness assumption. Our results suggest a positive effect (of up to 12.7 percent) of the language training programs on the re-employment probability, but no effects on wages for those who are observed employed regardless of training participation.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"8 1","pages":"1 - 44"},"PeriodicalIF":0.0,"publicationDate":"2022-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42577592","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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