28 citations · 58 across the 28 of their papers we have counts for
5 papers · 1 filter
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…
Nonparametric inverse probability weighted estimators based on the highly adaptive lasso
Ashkan Ertefaie, Nima S. Hejazi, Mark J. van der Laan
Inverse probability weighted estimators are the oldest and potentially most commonly used class of procedures for the estimation of causal effects. By adjusting for selection biase…
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…