Zobrazeno 1 - 10
of 245
pro vyhledávání: '"Ertefaie Ashkan"'
Autor:
Schnitzer, Mireille E, Talbot, Denis, Liu, Yan, Berger, David, Wang, Guanbo, O'Loughlin, Jennifer, Sylvestre, Marie-Pierre, Ertefaie, Ashkan
Causal variable selection in time-varying treatment settings is challenging due to evolving confounding effects. Existing methods mainly focus on time-fixed exposures and are not directly applicable to time-varying scenarios. We propose a novel two-s
Externí odkaz:
http://arxiv.org/abs/2410.08283
Publikováno v:
Journal of Causal Inference, Vol 8, Iss 1, Pp 300-314 (2020)
Doubly robust (DR) estimators are an important class of statistics derived from a theory of semiparametric efficiency. They have become a popular tool in causal inference, including applications to dynamic treatment regimes. The doubly robust estimat
Externí odkaz:
https://doaj.org/article/e31655dc7dd04966b9174a60b265db93
Publikováno v:
Journal of Causal Inference, Vol 6, Iss 1, Pp 550-560 (2018)
In the causal adjustment setting, variable selection techniques based only on the outcome or only on the treatment allocation model can result in the omission of confounders and hence may lead to bias, or the inclusion of spurious variables and hence
Externí odkaz:
https://doaj.org/article/1218b3619a964cdab2df392ef050319a
Marginal structural models have been widely used in causal inference to estimate mean outcomes under either a static or a prespecified set of treatment decision rules. This approach requires imposing a working model for the mean outcome given a seque
Externí odkaz:
http://arxiv.org/abs/2402.11466
Autor:
Jaman, Ajmery, Wang, Guanbo, Ertefaie, Ashkan, Bally, Michèle, Lévesque, Renée, Platt, Robert W., Schnitzer, Mireille E.
Effect modification occurs when the impact of the treatment on an outcome varies based on the levels of other covariates known as effect modifiers. Modeling these effect differences is important for etiological goals and for purposes of optimizing tr
Externí odkaz:
http://arxiv.org/abs/2402.00154
Flexible estimation of the mean outcome under a treatment regimen (i.e., value function) is the key step toward personalized medicine. We define our target parameter as a conditional value function given a set of baseline covariates which we refer to
Externí odkaz:
http://arxiv.org/abs/2309.16099
We study causal inference and efficient estimation for the expected number of recurrent events in the presence of a terminal event. We define our estimand as the vector comprising both the expected number of recurrent events and the failure survival
Externí odkaz:
http://arxiv.org/abs/2306.16571
In healthcare, there is much interest in estimating policies, or mappings from covariates to treatment decisions. Recently, there is also interest in constraining these estimated policies to the standard of care, which generated the observed data. A
Externí odkaz:
http://arxiv.org/abs/2306.14297
Publikováno v:
Statistics in medicine (2023)
Existing statistical methods can estimate a policy, or a mapping from covariates to decisions, which can then instruct decision makers (e.g., whether to administer hypotension treatment based on covariates blood pressure and heart rate). There is gre
Externí odkaz:
http://arxiv.org/abs/2211.16566
Autor:
Vo, Tat-Thang, Ye, Ting, Ertefaie, Ashkan, Roy, Samrat, Flory, James, Hennessy, Sean, Vansteelandt, Stijn, Small, Dylan S.
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 confounded by unmeasured confounders. Progress can be made if there is
Externí odkaz:
http://arxiv.org/abs/2209.10339