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The Role of Measured Covariates in Assessing Sensitivity to Unmeasured Confounding. 测量协变量在评估对未测量混杂的敏感性中的作用。
Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00008
Abhinandan Dalal, Iris Horng, Yang Feng, Dylan S Small
{"title":"The Role of Measured Covariates in Assessing Sensitivity to Unmeasured Confounding.","authors":"Abhinandan Dalal, Iris Horng, Yang Feng, Dylan S Small","doi":"10.1353/obs.00008","DOIUrl":"10.1353/obs.00008","url":null,"abstract":"<p><p>Sensitivity analysis is widely used to assess the robustness of causal conclusions in observational studies, yet its interaction with the structure of measured covariates is often overlooked. When latent confounders cannot be directly adjusted for and are instead controlled using proxy variables, strong associations between exposure and measured proxies can amplify sensitivity to residual confounding. We formalize this phenomenon in linear regression settings by showing that a simple ratio involving the exposure model coefficient and residual exposure variance provides an observable measure of this increased sensitivity. Applying our framework to smoking and lung cancer, we document how growing socioeconomic stratification in smoking behavior over time leads to heightened sensitivity to unmeasured confounding in more recent data. These results highlight the importance of multicollinearity when interpreting sensitivity analyses based on proxy adjustment.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 2","pages":"219-232"},"PeriodicalIF":0.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042566","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
The Cornfield Condition and Principle: A Critical Appraisal. 玉米田条件与原则：一种批判性评价。
Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00005
Tobias Freidling, Marco Piccininni
{"title":"The Cornfield Condition and Principle: A Critical Appraisal.","authors":"Tobias Freidling, Marco Piccininni","doi":"10.1353/obs.00005","DOIUrl":"10.1353/obs.00005","url":null,"abstract":"<p><p>Cornfield, Haenszel, Hammond, Lilienfeld, Shimkin, and Wynder (1959)'s seminal work made two major contributions that shape causal inference and epidemiology to this day. First, Cornfield et al. provided the first example of quantitative sensitivity analysis for unmeasured confounding; a result that became subsequently known as the Cornfield condition. Second, they advocated for the use of relative effect measures for causal inference and absolute effect measures in public health. This recommendation is also referred to as the Cornfield principle. This commentary critically re-examines Cornfield et al.'s landmark contributions. With the benefit of hindsight, we formalize their arguments, identify shortcomings, and contextualize their claims in the light of recent research.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 2","pages":"179-192"},"PeriodicalIF":0.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600450/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042629","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
Assessing Unmeasured Confounding in Observational Research via Bayesian Semiparametric Modeling. 利用贝叶斯半参数模型评估观测研究中的未测量混杂。
Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00007
Xinyi Xu, Steven N MacEachern, Bo Lu
{"title":"Assessing Unmeasured Confounding in Observational Research via Bayesian Semiparametric Modeling.","authors":"Xinyi Xu, Steven N MacEachern, Bo Lu","doi":"10.1353/obs.00007","DOIUrl":"10.1353/obs.00007","url":null,"abstract":"<p><p>It is challenging to infer a causal relationship with observational data. Untestable assumptions of ignorability or exchangeability are often utilized to facilitate causal effect estimation, which opens the door to arguments for and against the validity of a causal conclusion. In their 1959 seminal paper, Cornfield and colleagues provided sophisticated statistical reasoning for causal inference with observational data and made a key technical contribution to sensitivity analysis. Cornfield's idea paved the way for the development of modern tools for assessment of causality if ignorability assumptions fail. Compared with classical/frequentist approaches, Bayesian methods for sensitivity analysis are less fully developed. In this commentary, we first introduce a Bayesian semiparametric model for causal inference, then present a sensitivity analysis strategy for the Gaussian process model. Our method is easily interpretable and avoids restrictive parametric outcome assumptions. It can also be applied to both population-level and conditional causal effects.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 2","pages":"211-218"},"PeriodicalIF":0.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042595","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
The Journey of Cornfield's Inequalities and How They Shaped Modern Sensitivity Analysis. 康菲尔德的不平等之旅及其如何塑造了现代敏感性分析。
Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00003
Zhixin Lun
{"title":"The Journey of Cornfield's Inequalities and How They Shaped Modern Sensitivity Analysis.","authors":"Zhixin Lun","doi":"10.1353/obs.00003","DOIUrl":"10.1353/obs.00003","url":null,"abstract":"<p><p>Cornfield et al.'s 1959 paper introduced a seminal framework for evaluating the impact of unmeasured confounding by quantifying the strength an unobserved factor would need to overturn an observed association. This commentary revisits Cornfield's original inequalities, clarifying their interpretation, and highlighting the reverse-reasoning principle that underlies modern sensitivity analysis. We summarize key methodological extensions developed by later researchers-ranging from conditional independence formulations and multiplicative bounds to the sharpened limits that led to the E-value, and illustrate how these refinements broadened the applicability of Cornfield's logic. Finally, we discuss the continued influence of Cornfield-type reasoning across epidemiology and related fields, where sensitivity analysis has become an essential component of study design and causal interpretation. More than six decades later, Cornfield's insight remains a foundational pillar of causal inference, guiding researchers in assessing the robustness of empirical findings to unmeasured confounding.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 2","pages":"155-162"},"PeriodicalIF":0.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042617","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
Jerome Cornfield: Some Memories. 杰罗姆·康菲尔德：一些回忆。
Observational studies Pub Date : 2026-08-27 eCollection Date: 2026-01-01 DOI: 10.1353/obs.00015
Susan S Ellenberg
{"title":"Jerome Cornfield: Some Memories.","authors":"Susan S Ellenberg","doi":"10.1353/obs.00015","DOIUrl":"https://doi.org/10.1353/obs.00015","url":null,"abstract":"<p><p>Jerome Cornfield was a leading biostatistician of the mid-20th century, held in extremely high regard both by fellow statisticians and medical researchers. He was also beloved for his warmth, charm and wonderful sense of humor. I was fortunate to begin my career working with him; in this piece, I share some memorable experiences.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 2","pages":"323-328"},"PeriodicalIF":0.0,"publicationDate":"2026-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13600444/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149042549","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 Double Machine Learning Approach for Combining Experimental and Observational Studies. 结合实验和观察研究的双机器学习方法。
Observational studies Pub Date : 2026-04-15 eCollection Date: 2025-01-01 DOI: 10.1353/obs.2025.a973068
Harsh Parikh, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
{"title":"A Double Machine Learning Approach for Combining Experimental and Observational Studies.","authors":"Harsh Parikh, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky","doi":"10.1353/obs.2025.a973068","DOIUrl":"10.1353/obs.2025.a973068","url":null,"abstract":"<p><p>Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational studies, allowing practitioners to test for assumption violations and estimate treatment effects consistently. Our framework proposes a falsification test for external validity and ignorability under milder assumptions. We provide consistent treatment effect estimators even when one of the assumptions is violated. However, our no-free-lunch theorem highlights the necessity of accurately identifying the violated assumption for consistent treatment effect estimation. Through comparative analyses, we show our framework's superiority over existing data fusion methods. The practical utility of our approach is further exemplified by three real-world case studies, underscoring its potential for widespread application in empirical research.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"11 3","pages":"249-300"},"PeriodicalIF":0.0,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13155357/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870645","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
MonotonicityTest: An R Package for Efficient Nonparametric Monotonicity Testing. 一个R包，用于有效的非参数单调性测试。
Observational studies Pub Date : 2026-03-14 eCollection Date: 2026-01-01 DOI: 10.1353/obs.2026.a985147
Dylan Huynh, Layla Parast
{"title":"MonotonicityTest: An R Package for Efficient Nonparametric Monotonicity Testing.","authors":"Dylan Huynh, Layla Parast","doi":"10.1353/obs.2026.a985147","DOIUrl":"10.1353/obs.2026.a985147","url":null,"abstract":"<p><p>Monotonicity testing is a classic and longstanding problem in statistics, with particular importance in econometrics when analyzing data measured over time. More generally, monotonicity often underlies many statistical methods as a common condition, typically related to a true underlying function estimated from observed data. In this work, we introduce and describe the R package MontonicityTest which implements a nonparametric test of the null hypothesis that the conditional mean function <math><mi>E</mi> <mo>(</mo> <mi>Y</mi> <mrow><mo>|</mo></mrow> <mi>X</mi> <mo>=</mo> <mi>x</mi> <mo>)</mo></math> is monotone increasing (non-decreasing) in x given observed data on ( <math><mi>X</mi> <mo>,</mo> <mi>Y</mi></math> ). The test itself was introduced in Hall and Heckman (2000) but has thus far not been readily available. Our package leverages recursive least squares and is implemented in C++ using the Rcpp package, which significantly reduces computational time relative to a naive approach. We describe the package details and features, as well as illustrate the main functions with an application to simulated diabetes clinical trial data.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 1","pages":"123-142"},"PeriodicalIF":0.0,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13155350/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870828","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
Synthetic Regressing Control. 综合回归控制。
Observational studies Pub Date : 2026-03-14 eCollection Date: 2026-01-01 DOI: 10.1353/obs.2026.a985146
Rong Zhu
{"title":"Synthetic Regressing Control.","authors":"Rong Zhu","doi":"10.1353/obs.2026.a985146","DOIUrl":"10.1353/obs.2026.a985146","url":null,"abstract":"<p><p>Estimating weights in the synthetic control method, typically resulting in sparse weights where only a few control units have non-zero weights, involves an optimization procedure that selects and combines control units to closely match the treated unit. However, it is not uncommon for the linear combination of pre-treatment period outcomes for the control units, using nonnegative weights with the constraint that their sum equals one, to inadequately approximate the pre-treatment outcomes for the treated unit. To address the issue, this paper proposes a simple and effective method called Synthetic Regressing Control (SRC). The SRC method begins by performing the univariate linear regression to appropriately align the pre-treatment periods of the control units with the treated unit. Subsequently, a SRC estimator is obtained by synthesizing the regressed controls. To determine the weights in the synthesis procedure, we propose an approach that utilizes a criterion of unbiased risk estimator. Theoretically, we show that the synthesis way is asymptotically optimal in the sense of achieving the minimum loss of the infeasible best possible synthetic estimator. Extensive numerical experiments highlight the advantages of the SRC method.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 1","pages":"91-122"},"PeriodicalIF":0.0,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13155348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870751","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
Outcome-Assisted Multiple Imputation of Missing Treatments. 结果辅助缺失治疗的多重归算。
Observational studies Pub Date : 2026-03-14 eCollection Date: 2026-01-01 DOI: 10.1353/obs.2026.a985145
Joseph Feldman, Jerome P Reiter
{"title":"Outcome-Assisted Multiple Imputation of Missing Treatments.","authors":"Joseph Feldman, Jerome P Reiter","doi":"10.1353/obs.2026.a985145","DOIUrl":"10.1353/obs.2026.a985145","url":null,"abstract":"<p><p>We provide guidance on multiple imputation of missing at random treatments in observational studies. Specifically, analysts should account for both covariates and outcomes, i.e., not just use propensity scores, when imputing the missing treatments. To do so, we develop outcome-assisted multiple imputation of missing treatments: the analyst fits a regression for the outcome on the treatment indicator and covariates, which is used to sharpen the predictive probabilities for missing treatments under an estimated propensity score model. We derive an expression for the bias of the inverse probability weighted estimator for the average treatment effect under multiple imputation of missing treatments, and we show theoretically that this bias can be made small by using outcome-assisted multiple imputation. Simulations demonstrate empirically that outcome-assisted multiple imputation can offer better inferential properties than using the treatment assignment model alone. We illustrate the procedure in an analysis of data from the National Longitudinal Survey of Youth.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 1","pages":"67-90"},"PeriodicalIF":0.0,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13155361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870766","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
Matching with Multiple Criteria and Its Application to Health Disparities Research. 多标准匹配及其在健康差异研究中的应用
Observational studies Pub Date : 2026-03-14 eCollection Date: 2026-01-01 DOI: 10.1353/obs.2026.a985144
Chang Chen, Zhiyu Qian, Bo Zhang
{"title":"Matching with Multiple Criteria and Its Application to Health Disparities Research.","authors":"Chang Chen, Zhiyu Qian, Bo Zhang","doi":"10.1353/obs.2026.a985144","DOIUrl":"10.1353/obs.2026.a985144","url":null,"abstract":"<p><p>Matching is a popular nonparametric covariate adjustment strategy in empirical health services research. Matching helps construct two groups comparable in many baseline covariates but different in some key aspects under investigation. The Institute of Medicine (IOM) defines a health services disparity as the difference in accessing health services between members of racial or ethnic minorities not justified by the difference in health status or patients' preference. To estimate a disparity measure consistent with the IOM definition, we propose a statistical matching methodology that constructs matched comparison groups from, for instance, white men, that resemble the target group, for instance, black men, in some selected covariates while remaining identical to the white men population before matching in the remaining covariates. Using the proposed method, we investigated the disparity gaps between white men and black men in the US in prostate-specific antigen (PSA) screening based on the 2020 Behavioral Risk Factor Surveillance System (BFRSS) database. We found a widening PSA screening rate as the white matched comparison group increasingly resembles the black men group. Finally, we provide code that replicates the case study and a tutorial that enables users to design customized matched comparison groups satisfying multiple criteria.</p>","PeriodicalId":74335,"journal":{"name":"Observational studies","volume":"12 1","pages":"43-66"},"PeriodicalIF":0.0,"publicationDate":"2026-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13155351/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147870825","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
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