Observational studies最新文献

Beyond "No Unmeasured Confounding": Challenges and Opportunities for Cornfield-Style Reasoning in Modern Causal Inference. 超越“没有无法测量的混淆”：现代因果推理中玉米田式推理的挑战与机遇。

Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00011

Gary C N Hettinger, Audrey Renson, Herbert P Susmann, Iván Díaz

{"title":"Beyond \"No Unmeasured Confounding\": Challenges and Opportunities for Cornfield-Style Reasoning in Modern Causal Inference.","authors":"Gary C N Hettinger, Audrey Renson, Herbert P Susmann, Iván Díaz","doi":"10.1353/obs.00011","DOIUrl":"10.1353/obs.00011","url":null,"abstract":"Jerome Cornfield's (1959) analysis of smoking and lung cancer introduced a foundational principle for causal inference from observational data: causal conclusions should be evaluated not by the absence of assumption violations, but by the implausibility of the violations required to overturn them. In the decades since, causal inference research has made substantial progress in explicitly defining identification assumptions and improving estimation under those assumptions. As estimation theory has advanced, concerns about model misspecification have receded, and flexible semi-parametric methods now exist for increasingly complex causal questions. Despite this progress, there has been comparatively little success in extending Cornfield-style sensitivity analysis to these modern settings, limiting both the interpretability and practical adoption of contemporary causal methods. In this commentary, we revisit Cornfield's original framework and consider its amenability for four identification assumptions that have received growing attention beyond standard unmeasured confounding: positivity, interference, parallel trends, and sequential exchangeability. For each, we evaluate its accessibility to Cornfield-style reasoning, discussing challenges and opportunities for developing interpretable, calibrated sensitivity analyses. We argue that Cornfield-style reasoning is more naturally applicable to some assumptions than others, clarifying where such approaches are promising and where fundamental obstacles remain.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 2","pages":"261-278"},"PeriodicalIF":0.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042544","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

Getting to Know Worst-Case Confounders. 了解最糟糕的混淆因素。

Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00009

Melody Huang, Samuel D Pimentel

引用次数: 0

Commentary: Smoking and Lung Cancer: A Historical Perspective. 评论：吸烟和肺癌：一个历史的视角。

Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00002

Joel B Greenhouse, Christopher J Phillips

引用次数: 0

Using Large Language Models for Sensitivity Analysis in Causal Inference: Case Studies on Cornfield's Inequality and the E-Value. 在因果推理中使用大型语言模型进行敏感性分析：以Cornfield不等式和e值为例。

Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00013

Qingyan Xiang, Jiahao Zhang, Bojian Feng

{"title":"Using Large Language Models for Sensitivity Analysis in Causal Inference: Case Studies on Cornfield's Inequality and the E-Value.","authors":"Qingyan Xiang, Jiahao Zhang, Bojian Feng","doi":"10.1353/obs.00013","DOIUrl":"https://doi.org/10.1353/obs.00013","url":null,"abstract":"Sensitivity analysis methods such as the Cornfield's inequality and the E-value were developed to assess the robustness of observed associations against unmeasured confounding - a major challenge in observational studies. However, the calculation and interpretation of these methods can be difficult for clinicians and interdisciplinary researchers. Recent advances in large language models (LLMs) offer accessible tools that could assist sensitivity analyses, but their reliability in this context has not been studied. We assess four widely used LLMs, ChatGPT, Claude, DeepSeek, and Gemini, on their ability to conduct sensitivity analyses using the Cornfield's inequality and the E-value. We first extract study-specific information (exposures, outcomes, measured confounders, and effect estimates) from four published observational studies in different fields. Using such information, we develop structured prompts to assess the performance of the LLMs in three aspects: (1) accuracy of E-value calculation, (2) qualitative interpretation of robustness to unmeasured confounding, and (3) suggestion of possible unmeasured confounders. To our knowledge, there has been little prior work on using LLMs for sensitivity analysis, and this study is an early investigation in this area. The results show that ChatGPT, Claude, and Gemini accurately reproduce the E-values, whereas DeepSeek shows small biases. Qualitative conclusions from all the LLMs align with the magnitude of the E-values and the reported effect sizes, and all models identify biologically and epidemiologically plausible unmeasured confounders. These findings suggest that, when guided by structured prompts, LLMs can effectively assist in evaluating unmeasured confounding, and thereby can support study design and decision-making in observational studies.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 2","pages":"297-310"},"PeriodicalIF":0.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042709","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

Cornfield's Pragmatic Contribution to Policy Discourse: Continued. 康菲尔德对政策话语的实用主义贡献：续。

Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00004

Kenneth A Frank, Qinyun Lin, Spiro J Maroulis

{"title":"Cornfield's Pragmatic Contribution to Policy Discourse: Continued.","authors":"Kenneth A Frank, Qinyun Lin, Spiro J Maroulis","doi":"10.1353/obs.00004","DOIUrl":"10.1353/obs.00004","url":null,"abstract":"Cornfield et al.'s contributions included documenting epidemiological and policy debates regarding the effects of smoking on lung cancer. It was in the context of those debates that Cornfield et al. expressed sensitivity of the estimated effect of smoking to the dual components of confounding-relationship of a potential confounder (e.g., genes) to smoking (the focal predictor) and to lung cancer (the outcome). Current advancements have used various graphical and tabular representations to similarly express sensitivity to the dual components of confounding. As a representative of these advancements, we present Frank's Impact Threshold for a Confounding (ITCV), which resolves the dual components of confounding into a single term based on the product of the confounder's correlations with the focal predictor and with the outcome. We calculate the ITCV for an inference of an effect of kindergarten retention on achievement and provide benchmarks based on observed covariates to interpret the ITCV. Ultimately, any sensitivity analysis should only be applied after one has estimated the best possible model. The value of the sensitivity analysis is then to inform policy-oriented discourse among as wide a range of stakeholders as possible.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 2","pages":"163-178"},"PeriodicalIF":0.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042582","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

Modeling the Effects of Smoking on Lung Cancer. 模拟吸烟对肺癌的影响。

Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00006

Richard J Cook, Laura S Bumbulis, Sung Duk Kim, Paul S Albert

引用次数: 0

Sensitivity Analysis of the Consistency Assumption. 一致性假设的敏感性分析。

Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00012

Brian Knaeble, Qinyun Lin, Erich Kummerfeld, Kenneth A Frank

引用次数: 0

Sensitivity Analysis on the Odds Ratio Scale: Extending Cornfield's Conditions. 优势比量表的敏感性分析：扩展玉米地条件。

Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00010

Elena Stanghellini, Sara Geneletti

引用次数: 0

Editors' Note. 编者注。

Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00001

Andrew J Spieker, Nandita Mitra

引用次数: 0

Toward a Total Causal Error Framework: Extending Cornfield's Insights Across Causal Identification, Representation, and Measurement. 走向一个完全的因果错误框架：将康菲尔德的见解扩展到因果识别、表征和测量。

Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00014

Elizabeth Willow Eisenhauer, Daifeng Han, Brad Edwards, J Michael Brick

{"title":"Toward a Total Causal Error Framework: Extending Cornfield's Insights Across Causal Identification, Representation, and Measurement.","authors":"Elizabeth Willow Eisenhauer, Daifeng Han, Brad Edwards, J Michael Brick","doi":"10.1353/obs.00014","DOIUrl":"10.1353/obs.00014","url":null,"abstract":"Cornfield et al.'s (1959) analysis of smoking and lung cancer showed how explicit assumptions about unobserved confounding and selection mechanisms shape the boundary between statistical association and causal conclusions. Today, causal inference spans diverse fields including economics, statistics, psychology, epidemiology, and evaluation, with distinct traditions and tools. Without a shared framework, it can be difficult to accumulate evidence across studies or to diagnose why results differ when similar questions are studied independently, particularly when differences arise from upstream design and data-generation choices rather than analytic methods alone. While tools for causal identification such as Directed Acyclic Graphs (DAGs) have become widely used across disciplines, they do not naturally foreground threats arising from representation and measurement errors. In contrast, the Total Survey Error (TSE) framework enumerates representation and measurement errors across the survey life cycle and is routinely used to evaluate tradeoffs in survey design and data collection. We propose a Total Causal Error (TCE) framework that integrates causal inference with the TSE perspective to organize risks to causal conclusions along parallel paths of causal identification, representation, and measurement, across the stages of the theoretical estimand, empirical estimand, data collection, data processing, and estimation and interpretation. We propose a version of this framework, provide explicit definitions for its components, illustrate a small number of use cases, and invite further development and coordination across fields.","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 2","pages":"311-322"},"PeriodicalIF":0.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042603","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