3 citations · 4 across the 4 of their papers we have counts for
8 papers
Structural mean models for instrumented difference-in-differences
Tat-Thang Vo, Ting Ye, Ashkan Ertefaie +5
In the standard difference-in-differences research design, the parallel trends assumption may be violated when the relationship between the exposure trend and the outcome trend is…
A non-parametric Bayesian approach for adjusting partial compliance in sequential decision making
Indrabati Bhattacharya, Brent A. Johnson, William Artman +4
Existing methods in estimating the mean outcome under a given dynamic treatment regime rely on intention-to-treat analyses which estimate the effect of following a certain dynamic…
Instrumented Difference-in-Differences
Ting Ye, Ashkan Ertefaie, James Flory +2
Unmeasured confounding is a key threat to reliable causal inference based on observational studies. Motivated from two powerful natural experiment devices, the instrumental variabl…
Bayesian Set of Best Dynamic Treatment Regimes and Sample Size Determination for SMARTs with Binary Outcomes
William J. Artman, Ashkan Ertefaie, Kevin G. Lynch +1
One of the main goals of sequential, multiple assignment, randomized trials (SMART) is to find the most efficacious design embedded dynamic treatment regimes. The analysis method k…
Adjusting for Partial Compliance in SMARTs: a Bayesian Semiparametric Approach
William J. Artman, Ashkan Ertefaie, Kevin G. Lynch +2
The cyclical and heterogeneous nature of many substance use disorders highlights the need to adapt the type or the dose of treatment to accommodate the specific and changing needs…
Robust Q-learning
Ashkan Ertefaie, James R. McKay, David Oslin +1
Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy. Finite dimensional working models are typically…