activity
20172021
most citedEstimating effects within nonlinear autoregressive models: a case study on the impact of child access prevention laws on firearm mortality

5 citations · 11 across the 3 of their papers we have counts for

collaborators
Showing stat.MEShow all

6 papers · 1 filter

stat.ME20215 cited

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…

stat.ME20203 cited

Reducing bias in difference-in-differences models using entropy balancing

Matthew Cefalu, Brian G. Vegetabile, Michael Dworsky +2

This paper illustrates the use of entropy balancing in difference-in-differences analyses when pre-intervention outcome trends suggest a possible violation of the parallel trends a…

stat.ME2020

Nonparametric Estimation of Population Average Dose-Response Curves using Entropy Balancing Weights for Continuous Exposures

Brian G. Vegetabile, Beth Ann Griffin, Donna L. Coffman +2

Weighted estimators are commonly used for estimating exposure effects in observational settings to establish causal relations. These estimators have a long history of development w…

stat.ME2019

Synthetic estimation for the complier average causal effect

Denis Agniel, Bing Han, Matthew Cefalu

We propose an improved estimator of the complier average causal effect (CACE). Researchers typically choose a presumably-unbiased estimator for the CACE in studies with noncomplian…

stat.ME2019

Averaging causal estimators in high dimensions

Joseph Antonelli, Matthew Cefalu

There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large. This…

stat.ME20173 cited

Posterior Predictive Treatment Assignment for Estimating Causal Effects with Limited Overlap

Corwin M Zigler, Matthew Cefalu

Estimating causal effects with propensity scores relies upon the availability of treated and untreated units observed at each value of the estimated propensity score. In settings w…