Observational studies最新文献

Commentary on ``Nonparametric identification is not enough, but randomized controlled trials are'': Statistical considerations for generating reliable evidence across a spectrum of studies that increasingly involve real-world elements. 对“非参数识别是不够的，但随机对照试验是”的评论：在越来越多地涉及现实世界元素的一系列研究中产生可靠证据的统计考虑。

Observational studies Pub Date : 2025-04-11 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a956842

Rachael Phillips, Mark van der Laan

{"title":"Commentary on ``Nonparametric identification is not enough, but randomized controlled trials are'': Statistical considerations for generating reliable evidence across a spectrum of studies that increasingly involve real-world elements.","authors":"Rachael Phillips, Mark van der Laan","doi":"10.1353/obs.2025.a956842","DOIUrl":"10.1353/obs.2025.a956842","url":null,"abstract":"Judea Pearl, quoted in Pearl and Mackenzie (2008), stated that \"once we have understood why [randomized controlled trials] RCTs work, there is no need to put them on a pedestal and treat them as the gold standard of causal analysis, which all other methods should emulate.\" In Aronow et al. (2024), this claim is refuted, drawing on results of Robins and Ritov (1997). The argument is made that statistical estimation and inference tend to be fundamentally more difficult in observational studies than in randomized controlled trials, even when all confounders are observed and measured without error. We congratulate the authors for raising this highly timely, interesting discussion and welcome this opportunity to join this important debate. In this commentary, we focus on what it takes to generate reliable evidence across a spectrum of studies that increasingly involve real-world elements and less control over design. A related question is whether, along this spectrum of studies, the reliability of evidence generated by a statistical analysis decreases. We claim that this is not the case, but that the challenge for the appropriate statistical method increases, requiring sophisticated and careful execution.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"11 1","pages":"61-76"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12139718/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonparametric identification is not enough, but randomized controlled trials are. 非参数识别是不够的，但随机对照试验。

Observational studies Pub Date : 2025-04-11 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a956837

P M Aronow, James M Robins, Theo Saarinen, Fredrik Sävje, Jasjeet S Sekhon

引用次数: 0

Why are RCTs the Gold Standard? The Epistemological Difference Between Randomized Experiments and Observational Studies. 为什么随机对照试验是黄金标准？随机实验与观察性研究的认识论差异。

Observational studies Pub Date : 2025-04-11 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a956840

Christopher Harshaw

引用次数: 0

Enough? 足够了吗?

Observational studies Pub Date : 2025-04-11 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a956838

Drew Dimmery, Kevin Munger

{"title":"Enough?","authors":"Drew Dimmery, Kevin Munger","doi":"10.1353/obs.2025.a956838","DOIUrl":"10.1353/obs.2025.a956838","url":null,"abstract":"We provide a critical response to Aronow et al. (2021) which argued that randomized controlled trials (RCTs) are \"enough,\" while nonparametric identification in observational studies is not. We first investigate what is meant by \"enough,\" arguing that this is a fundamentally a sociological claim about the relationship between statistical work and relevant institutional processes (here, academic peer review), rather than something that can be decided from within the logic of statistics. For a more complete conception of \"enough,\" we outline all that would need to be known - not just knowledge of propensity scores, but knowledge of many other spatial and temporal characteristics of the social world. Even granting the logic of the critique in Aronow et al. (2021), its practical importance is a question of the contexts under study. We argue that we should not be satisfied by appeals to intuition or experience about the complexity of \"naturally occurring\" propensity score functions. Instead, we call for more empirical metascience to begin to characterize this complexity. We apply this logic to the case of recommender systems as a demonstration of the weakness of allowing statisticians' intuitions to serve in place of metascientific data. This may be, as Aronow et al. (2021) claim, one of the \"few free lunches in statistics\"-but like many of the free lunches consumed by statisticians, it is only available to those working at a handful of large tech firms. Rather than implicitly deciding what is \"enough\" based on statistical applications the social world has determined to be most profitable, we are argue that practicing statisticians should explicitly engage with questions like \"for what?\" and \"for whom?\" in order to adequately answer the question of \"enough?\"","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"11 1","pages":"17-26"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12139716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251201","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rejoinder: Nonparametric identification is not enough, but randomized controlled trials are. 反驳：非参数识别是不够的，但随机对照试验是足够的。

Observational studies Pub Date : 2025-04-11 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a956844

P M Aronow, James M Robins, Theo Saarinen, Fredrik Sävje, Jasjeet S Sekhon

{"title":"Rejoinder: Nonparametric identification is not enough, but randomized controlled trials are.","authors":"P M Aronow, James M Robins, Theo Saarinen, Fredrik Sävje, Jasjeet S Sekhon","doi":"10.1353/obs.2025.a956844","DOIUrl":"10.1353/obs.2025.a956844","url":null,"abstract":"We thank the editor for organizing a diverse and wide-ranging discussion, and we thank the commentators for their detailed and thoughtful remarks. Most of the commentators provide broader perspectives on randomized experiments and their role in modern empirical practice. We believe this broader perspective is important, and the comments serve as complements to the somewhat narrow points we made in our paper. However, we believe these narrow points are of great consequence, and we find it useful to briefly recapitulate them here. When a practitioner aims to estimate averages of bounded potential outcomes (e.g., the average treatment effect on a binary outcome) in a setting where both ignorability and positivity are known to hold after adjusting for at least one continuous covariate, the following statements are true: • If the propensity score is known, such as in a randomized controlled trial (RCT), there exist simple estimators that are uniformly root-n consistent and asymptotically normal. Confidence intervals based on these estimators are finite-sample valid and their widths shrink at a root-n rate. • If the propensity score is not known, such as in an observational study, there exist neither uniformly consistent estimators nor uniform (i.e., honest) large-sample confidence intervals whose widths are shrinking with the sample size. To achieve these properties, the practitioner must impose untestable assumptions on either the propensity score function or the conditional expectation function of the outcomes.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"11 1","pages":"85-90"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12139717/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Bureaucratic Theory of Statistics. 统计的官僚理论。

Observational studies Pub Date : 2025-04-11 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a956843

Benjamin Recht

引用次数: 0

Priors and Propensity Scores in Bayesian Causal Inference. 贝叶斯因果推理中的先验和倾向得分。

Observational studies Pub Date : 2025-04-11 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a956841

Arman Oganisian, Antonio Linero

{"title":"Priors and Propensity Scores in Bayesian Causal Inference.","authors":"Arman Oganisian, Antonio Linero","doi":"10.1353/obs.2025.a956841","DOIUrl":"10.1353/obs.2025.a956841","url":null,"abstract":"Aronow et al. (2025) provide a convincing case for the special status of randomized controlled trials (RCTs) in which the propensity scores are known and can be used to make causal inferences. Here we provide a Bayesian perspective on their work by summarizing recent developments in the Bayesian literature on the topic. Whether the propensity score should play a role in Bayesian causal inference - and what that role(s) should be - has been a controversial topic for some time. We begin by describing Bayesian inference for population-level estimands and show that under commonly made (but not necessarily required) assumptions, the propensity score model has no role to play in Bayesian causal inference from a purist perspective. We discuss recent work on why these assumptions can be problematic - particularly in high-dimensional models - and discuss several Bayesian motivations for relaxing them. We describe out recent approaches for incorporating the propensity score correspond to di erent ways of relaxing these assumptions. Given these considerations, we illustrate how a Bayesian might approach the synethic examples of Aronow et al. (2025).","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"11 1","pages":"47-60"},"PeriodicalIF":0.0,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12139722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144251203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

What randomization can and cannot guarantee. 随机化能保证什么，不能保证什么。

Observational studies Pub Date : 2025-04-11 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a956839

Peng Ding

引用次数: 0

Does matching introduce confounding or selection bias into the matched case-control design? 匹配病例对照设计是否会引入混杂或选择偏差？

Observational studies Pub Date : 2024-06-06 DOI: 10.1353/obs.2024.a929114

Fei Wan, S. Sutcliffe, Jeffrey Zhang, Dylan Small

{"title":"Does matching introduce confounding or selection bias into the matched case-control design?","authors":"Fei Wan, S. Sutcliffe, Jeffrey Zhang, Dylan Small","doi":"10.1353/obs.2024.a929114","DOIUrl":"https://doi.org/10.1353/obs.2024.a929114","url":null,"abstract":"Abstract:The impact of matching on confounding control in case-control studies remains a subject of ongoing debate, with varying perspectives among researchers. While matching is a well-established method for controlling confounding in cohort studies, its effectiveness in mitigating confounding in case-control studies has long been questioned. Recent studies have determined that matching doesn't eliminate confounding but, instead, introduces a selection bias on top of the initial confounding, as indicated by causal diagram analysis. This conclusion suggests that the control of initial confounding through matching is either only partial or non-existent. However, this conclusion may not be accurate in exactly matched design because causal diagram cannot always reveal precisely the interplay between the initial confounding and the matching induced selection effect. In this paper, we employ analytical results in conjunction with causal diagrams to demonstrate that the cancellation of the initial confounding by the selection effect is complete in exact individually matched case-control studies. Nevertheless, this cancellation results in a residual selection effect that establishes a backdoor connection between the matching factors and the outcome in the matched design. Failure to adjust for this residual selection effect leads to biased estimates of the exposure effect. Furthermore, this backdoor connection causes matching factors to act like confounding factors in the matched case-control design, which complicates the interpretation of the bias introduced by matching in current literature.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"321 4","pages":"1 - 9"},"PeriodicalIF":0.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141381359","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}

引用次数: 0

Using a difference-in-difference control trial to test an intervention aimed at increasing the take-up of a welfare payment in New Zealand 使用差异中差异对照试验来测试旨在增加新西兰福利支付的干预措施

Observational studies Pub Date : 2023-09-07 DOI: 10.1353/obs.2023.a906626

David Rea, Dean R. Hyslop

引用次数: 0