19 citations · 56 across the 16 of their papers we have counts for
21 papers
Gaussian comparison above the median
Colin B. Fogarty
We prove a Gaussian comparison inequality for closed convex sets with reference probability at least 1/2. For centered Gaussian vectors whose covariance matrices are ordered in the…
Stochastic Sensitivity Analysis for Matched Observational Studies
Mengqi Lin, Colin B. Fogarty, Gongjun Xu
Sensitivity analysis asks how strong unmeasured confounding needs to be to explain away an observational study's conclusion. The conventional approach in matched studies conducts i…
Powerful Multivariate Sensitivity Analysis via Sample Splitting in an Observational Study of the Effects of Poverty on Cardiovascular Disease Risk Factors
William Bekerman, Anurag Mehta, Rebecca E. Hasson +3
When assessing the causal effect of an exposure on two or more outcomes in an observational study, a linear combination of outcomes may lessen the sensitivity of a test of the glob…
Simultaneous Inference for False Discovery Proportions under Sensitivity Models for Observational Studies
Mengqi Lin, Colin Fogarty
We provide an approach to exploratory data analysis in observational studies with a single intervention and multiple endpoints. In such settings, the researcher would like to explo…
Tilted sensitivity analysis in matched observational studies
Colin B. Fogarty
We present a new procedure for conducting a sensitivity analysis in matched observational studies. For any candidate test statistic, the approach defines tilted modifications depen…
Heterogeneous Treatment Effects under Network Interference: A Nonparametric Approach Based on Node Connectivity
Heejong Bong, Colin B. Fogarty, Elizaveta Levina +1
In network settings, interference between units makes causal inference more challenging as outcomes may depend on the treatments received by others in the network. Typical estimand…
Prepivoted permutation tests
Colin B. Fogarty
We present a general approach to constructing permutation tests that are both exact for the null hypothesis of equality of distributions and asymptotically correct for testing equa…
No-harm calibration for generalized Oaxaca-Blinder estimators
Peter L. Cohen, Colin B. Fogarty
In randomized experiments, adjusting for observed features when estimating treatment effects has been proposed as a way to improve asymptotic efficiency. However, only linear regre…
Gaussian Prepivoting for Finite Population Causal Inference
Peter L. Cohen, Colin B. Fogarty
In finite population causal inference exact randomization tests can be constructed for sharp null hypotheses, i.e. hypotheses which fully impute the missing potential outcomes. Oft…
Biased Encouragements and Heterogeneous Effects in an Instrumental Variable Study of Emergency General Surgical Outcomes
Colin B. Fogarty, Kwonsang Lee, Rachel R. Kelz +1
We investigate the efficacy of surgical versus non-surgical management for two gastrointestinal conditions, colitis and diverticulitis, using observational data. We deploy an instr…
Testing weak nulls in matched observational studies
Colin B. Fogarty
We develop sensitivity analyses for weak nulls in matched observational studies while allowing unit-level treatment effects to vary. The methods may be applied to studies using any…
Increasing Power for Observational Studies of Aberrant Response: An Adaptive Approach
Siyu Heng, Hyunseung Kang, Dylan S. Small +1
In many observational studies, the interest is in the effect of treatment on bad, aberrant outcomes rather than the average outcome. For such settings, the traditional approach is…
Patterns of Effects and Sensitivity Analysis for Differences-in-Differences
Luke J. Keele, Dylan S. Small, Jesse Y. Hsu +1
Applied analysts often use the differences-in-differences (DID) method to estimate the causal effect of policy interventions with observational data. The method is widely used, as…
Multivariate one-sided testing in matched observational studies as an adversarial game
Peter L. Cohen, Matt A. Olson, Colin B. Fogarty
We present a multivariate one-sided sensitivity analysis for matched observational studies, appropriate when the researcher has specified that a given causal mechanism should manif…
Extended Sensitivity Analysis for Heterogeneous Unmeasured Confounding with An Application to Sibling Studies of Returns to Education
Colin B. Fogarty, Raiden B. Hasegawa
The conventional model for assessing insensitivity to hidden bias in paired observational studies constructs a worst-case distribution for treatment assignments subject to bounds o…
On mitigating the analytical limitations of finely stratified experiments
Colin B. Fogarty
While attractive from a theoretical perspective, finely stratified experiments such as paired designs suffer from certain analytical limitations not present in block-randomized exp…
Regression assisted inference for the average treatment effect in paired experiments
Colin B. Fogarty
In paired randomized experiments individuals in a given matched pair may differ on prognostically important covariates despite the best efforts of practitioners. We examine the use…
Studentized sensitivity analysis for the sample average treatment effect in paired observational studies
Colin B. Fogarty
A fundamental limitation of causal inference in observational studies is that perceived evidence for an effect might instead be explained by factors not accounted for in the primar…
Sensitivity Analysis for Multiple Comparisons in Matched Observational Studies through Quadratically Constrained Linear Programming
Colin B. Fogarty, Dylan S. Small
A sensitivity analysis in an observational study assesses the robustness of significant findings to unmeasured confounding. While sensitivity analyses in matched observational stud…
Discrete Optimization for Interpretable Study Populations and Randomization Inference in an Observational Study of Severe Sepsis Mortality
Colin B. Fogarty, Mark E. Mikkelsen, David F. Gaieski +1
Motivated by an observational study of the effect of hospital ward versus intensive care unit admission on severe sepsis mortality, we develop methods to address two common problem…
Equivalence testing for functional data with an application to comparing pulmonary function devices
Colin B. Fogarty, Dylan S. Small
Equivalence testing for scalar data has been well addressed in the literature, however, the same cannot be said for functional data. The resultant complexity from maintaining the f…