Zobrazeno 1 - 10
of 158
pro vyhledávání: '"Santacatterina Michele"'
Autor:
Kallus Nathan, Santacatterina Michele
Publikováno v:
Journal of Causal Inference, Vol 10, Iss 1, Pp 123-140 (2022)
In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects. Ad hoc methods have been developed for each est
Externí odkaz:
https://doaj.org/article/899d97d9d0df482abd9d770f2809d804
Autor:
Kallus Nathan, Santacatterina Michele
Publikováno v:
Journal of Causal Inference, Vol 9, Iss 1, Pp 345-369 (2021)
Marginal structural models (MSMs) can be used to estimate the causal effect of a potentially time-varying treatment in the presence of time-dependent confounding via weighted regression. The standard approach of using inverse probability of treatment
Externí odkaz:
https://doaj.org/article/465cea9f35504176bb3c9ec023c5a7cf
Marginal structural models are a popular method for estimating causal effects in the presence of time-varying exposures. In spite of their popularity, no scalable non-parametric estimator exist for marginal structural models with multi-valued and tim
Externí odkaz:
http://arxiv.org/abs/2409.18782
Modeling multivariate time series is a well-established problem with a wide range of applications from healthcare to financial markets. Traditional State Space Models (SSMs) are classical approaches for univariate time series modeling due to their si
Externí odkaz:
http://arxiv.org/abs/2406.04320
Platform trials are multi-arm designs that simultaneously evaluate multiple treatments for a single disease within the same overall trial structure. Unlike traditional randomized controlled trials, they allow treatment arms to enter and exit the tria
Externí odkaz:
http://arxiv.org/abs/2404.19118
Recent advances in deep learning have mainly relied on Transformers due to their data dependency and ability to learn at scale. The attention module in these architectures, however, exhibits quadratic time and space in input size, limiting their scal
Externí odkaz:
http://arxiv.org/abs/2403.19888
Autor:
Pham, Khiem, Hirshberg, David A., Huynh-Pham, Phuong-Mai, Santacatterina, Michele, Lim, Ser-Nam, Zabih, Ramin
We propose an empirically stable and asymptotically efficient covariate-balancing approach to the problem of estimating survival causal effects in data with conditionally-independent censoring. This addresses a challenge often encountered in state-of
Externí odkaz:
http://arxiv.org/abs/2310.02278
Estimating causal effects from large experimental and observational data has become increasingly prevalent in both industry and research. The bootstrap is an intuitive and powerful technique used to construct standard errors and confidence intervals
Externí odkaz:
http://arxiv.org/abs/2302.02859
Autor:
Shu, Michelle, Bowen, Richard Strong, Herrmann, Charles, Qi, Gengmo, Santacatterina, Michele, Zabih, Ramin
Time-to-event analysis is an important statistical tool for allocating clinical resources such as ICU beds. However, classical techniques like the Cox model cannot directly incorporate images due to their high dimensionality. We propose a deep learni
Externí odkaz:
http://arxiv.org/abs/2108.09641
Autor:
Nemani, Katlyn, De Picker, Livia, Dickerson, Faith, Leboyer, Marion, Santacatterina, Michele, Ando, Fumika, Capichioni, Gillian, Smith, Thomas E., Kammer, Jamie, El Abdellati, Kawtar, Morrens, Manuel, Coppens, Violette, Katsafanas, Emily, Origoni, Andrea, Khan, Sabahat, Rowe, Kelly, Ziemann, R.Sarah, Tamouza, Ryad, Yolken, Robert H., Goff, Donald C.
Publikováno v:
In Brain, Behavior, & Immunity - Health July 2024 38