16 citations · 86 across the 18 of their papers we have counts for
22 papers
Commentary on Guyll et al. (2023): Misuse of Statistical Method Results in Highly Biased Interpretation of Forensic Evidence
Michael Rosenblum, Elizabeth T. Chin, Elizabeth L. Ogburn +6
Since the National Academy of Sciences released their report outlining paths for improving reliability, standards, and policies in the forensic sciences NAS (2009), there has been…
Consistency of common spatial estimators under spatial confounding
Brian Gilbert, Elizabeth L. Ogburn, Abhirup Datta
This paper addresses the asymptotic performance of popular spatial regression estimators of the linear effect of an exposure on an outcome under ``spatial confounding" -- the prese…
Graphical Models of Entangled Missingness
Ranjani Srinivasan, Rohit Bhattacharya, Razieh Nabi +2
Despite the growing interest in causal and statistical inference for settings with data dependence, few methods currently exist to account for missing data in dependent data settin…
Augmented balancing weights as linear regression
David Bruns-Smith, Oliver Dukes, Avi Feller +1
We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular doubly robust or de-biased machine le…
Sensitivity analysis for principal ignorability violation in estimating complier and noncomplier average causal effects
Trang Quynh Nguyen, Elizabeth A. Stuart, Daniel O. Scharfstein +1
An important strategy for identifying principal causal effects, which are often used in settings with noncompliance, is to invoke the principal ignorability (PI) assumption. As PI…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…
A causal inference framework for spatial confounding
Brian Gilbert, Abhirup Datta, Joan A. Casey +1
Over the past few decades, addressing "spatial confounding" has become a major topic in spatial statistics. However, the literature has provided conflicting definitions, and many p…
Causal mediation analysis: From simple to more robust strategies for estimation of marginal natural (in)direct effects
Trang Quynh Nguyen, Elizabeth L. Ogburn, Ian Schmid +4
This paper aims to provide practitioners of causal mediation analysis with a better understanding of estimation options. We take as inputs two familiar strategies (weighting and mo…
Clarifying causal mediation analysis: Effect identification via three assumptions and five potential outcomes
Trang Quynh Nguyen, Ian Schmid, Elizabeth L. Ogburn +1
Causal mediation analysis is complicated with multiple effect definitions that require different sets of assumptions for identification. This paper provides a systematic explanatio…
Identifying effects of multiple treatments in the presence of unmeasured confounding
Wang Miao, Wenjie Hu, Elizabeth L. Ogburn +1
Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences. The problem of unmeasured con…
Causal Effects in Twin Studies: the Role of Interference
Bonnie Smith, Elizabeth L. Ogburn, Matt McGue +2
The use of twins designs to address causal questions is becoming increasingly popular. A standard assumption is that there is no interference between twins---that is, no twin's exp…
Counterexamples to "The Blessings of Multiple Causes" by Wang and Blei
Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen
This note has been updated (April, 2020) to respond to "Towards Clarifying the Theory of the Deconfounder" by Yixin Wang, David M. Blei (arXiv:2003.04948). This original note, post…
Comment on "Blessings of Multiple Causes"
Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen
(This comment has been updated to respond to Wang and Blei's rejoinder [arXiv:1910.07320].) The premise of the deconfounder method proposed in "Blessings of Multiple Causes" by Wan…
Network Dependence Can Lead to Spurious Associations and Invalid Inference
Youjin Lee, Elizabeth L. Ogburn
Researchers across the health and social sciences generally assume that observations are independent, even while relying on convenience samples that draw subjects from one or a sma…
Testing for Network and Spatial Autocorrelation
Youjin Lee, Elizabeth L. Ogburn
Testing for dependence has been a well-established component of spatial statistical analyses for decades. In particular, several popular test statistics have desirable properties f…
Challenges to estimating contagion effects from observational data
Elizabeth L. Ogburn
A growing body of literature attempts to learn about contagion using observational (i.e. non-experimental) data collected from a single social network. While the conclusions of the…
Causal inference for social network data
Elizabeth L. Ogburn, Oleg Sofrygin, Ivan Diaz +1
We describe semiparametric estimation and inference for causal effects using observational data from a single social network. Our asymptotic results are the first to allow for depe…
The magnitude and direction of collider bias for binary variables
Trang Quynh Nguyen, Allan Dafoe, Elizabeth L. Ogburn
Suppose we are interested in the effect of variable on variable . If and both influence, or are associated with variables that influence, a common outcome, called a…
High-dimensional Multivariate Mediation: with Application to Neuroimaging Data
Oliver Y. Chén, Ciprian M. Crainiceanu, Elizabeth L. Ogburn +3
Mediation analysis has become an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a randomized treatment…
Incorporating Contact Network Structure in Cluster Randomized Trials
Patrick C. Staples, Elizabeth L. Ogburn, Jukka-Pekka Onnela
Whenever possible, the efficacy of a new treatment, such as a drug or behavioral intervention, is investigated by randomly assigning some individuals to a treatment condition and o…
Vaccines, Contagion, and Social Networks
Elizabeth L. Ogburn, Tyler J. VanderWeele
Consider the causal effect that one individual's treatment may have on another individual's outcome when the outcome is contagious, with specific application to the effect of vacci…
Causal Diagrams for Interference
Elizabeth L. Ogburn, Tyler J. VanderWeele
The term "interference" has been used to describe any setting in which one subject's exposure may affect another subject's outcome. We use causal diagrams to distinguish among thre…