no code implementations • 28 Sep 2023 • Ashkan Ertefaie, Luke Duttweiler, Brent A. Johnson, Mark J. Van Der Laan
Third, we provide consistency and convergence rate for the optimizer of the regimen-response curve estimator; this enables us to estimate an optimal semiparametric rule.
no code implementations • 28 Jun 2023 • Benjamin R. Baer, Robert L. Strawderman, Ashkan Ertefaie
We study causal inference and efficient estimation for the expected number of recurrent events in the presence of a terminal event.
1 code implementation • 25 Jun 2023 • Samuel J. Weisenthal, Sally W. Thurston, Ashkan Ertefaie
Further, in the relative sparsity work, the authors only considered the single-stage decision case; here, we consider the more general, multi-stage case.
1 code implementation • 29 Nov 2022 • Samuel J. Weisenthal, Sally W. Thurston, Ashkan Ertefaie
This end is facilitated if one can pinpoint the aspects of the policy (i. e., the parameters for blood pressure and heart rate) that change when moving from the standard of care to the new, suggested policy.
1 code implementation • 6 Nov 2020 • Ting Ye, Ashkan Ertefaie, James Flory, Sean Hennessy, Dylan S. Small
Unmeasured confounding is a key threat to reliable causal inference based on observational studies.
Causal Inference Methodology
2 code implementations • 22 May 2020 • Ashkan Ertefaie, Nima S. Hejazi, Mark J. Van Der Laan
We propose a class of nonparametric inverse probability weighted estimators in which the weighting mechanism is estimated via undersmoothing of the highly adaptive lasso, a nonparametric regression function proven to converge at $n^{-1/3}$-rate to the true weighting mechanism.
no code implementations • 27 Mar 2020 • Ashkan Ertefaie, James R. McKay, David Oslin, Robert L. Strawderman
Q-learning is a regression-based approach that is widely used to formalize the development of an optimal dynamic treatment strategy.
1 code implementation • Biometrics 2017 • Susan M Shortreed, Ashkan Ertefaie
Traditionally, a “throw in the kitchen sink” approach has been used to select covariates for inclusion into the propensity score, but recent work shows including unnecessary covariates can impact both the bias and statistical efficiency of propensity score estimators.
no code implementations • 3 Jun 2014 • Ashkan Ertefaie
The application of existing methods for constructing optimal dynamic treatment regimes is limited to cases where investigators are interested in optimizing a utility function over a fixed period of time (finite horizon).