20 citations · 45 across the 11 of their papers we have counts for
5 papers · 2 filters
CoBWeb: a user-friendly web application to estimate causal treatment effects from observational data using multiple algorithms
Andreas Markoulidakis, Peter Holmans, Philip Pallmann +2
Background/aims: While randomized controlled trials are the gold standard for measuring causal effects, robust conclusions about causal relationships can be obtained using data fro…
Estimating effects within nonlinear autoregressive models: a case study on the impact of child access prevention laws on firearm mortality
Matthew Cefalu, Terry Schell, Beth Ann Griffin +2
Autoregressive models are widely used for the analysis of time-series data, but they remain underutilized when estimating effects of interventions. This is in part due to endogenei…
How balance and sample size impact bias in the estimation of causal treatment effects: A simulation study
Andreas Markoulidakis, Peter Holmans, Philip Pallmann +2
Observational studies are often used to understand relationships between exposures and outcomes. They do not, however, allow conclusions about causal relationships to be drawn unle…
Balancing Higher Moments Matters for Causal Estimation: Further Context for the Results of Setodji et al. (2017)
Melody Y. Huang, Brian G. Vegetabile, Lane F. Burgette +2
We expand upon the simulation study of Setodji et al. (2017) which compared three promising balancing methods when assessing the average treatment effect on the treated for binary…
Methodological considerations for estimating policy effects in the context of co-occurring policies
Beth Ann Griffin, Megan S. Schuler, Joseph Pane +5
Understanding how best to estimate state-level policy effects is important, and several unanswered questions remain, particularly about the ability of statistical models to disenta…