Zobrazeno 1 - 10
of 1 328
pro vyhledávání: '"Dominici, Francesca"'
Autor:
Zorzetto, Dafne, Canale, Antonio, Mealli, Fabrizia, Dominici, Francesca, Bargagli-Stoffi, Falco J.
Principal stratification provides a causal inference framework that allows adjustment for confounded post-treatment variables when comparing treatments. Although the literature has focused mainly on binary post-treatment variables, there is a growing
Externí odkaz:
http://arxiv.org/abs/2405.17669
Autor:
Battiloro, Claudio, Karaismailoğlu, Ege, Tec, Mauricio, Dasoulas, George, Audirac, Michelle, Dominici, Francesca
Graph neural networks excel at modeling pairwise interactions, but they cannot flexibly accommodate higher-order interactions and features. Topological deep learning (TDL) has emerged recently as a promising tool for addressing this issue. TDL enable
Externí odkaz:
http://arxiv.org/abs/2405.15429
Autor:
Considine, Ellen M., Nethery, Rachel C., Wellenius, Gregory A., Dominici, Francesca, Tec, Mauricio
A key strategy in societal adaptation to climate change is the use of alert systems to reduce the adverse health impacts of extreme heat events by prompting preventative action. In this work, we investigate reinforcement learning (RL) as a tool to op
Externí odkaz:
http://arxiv.org/abs/2312.14196
Autor:
Tec, Mauricio, Trisovic, Ana, Audirac, Michelle, Woodward, Sophie, Hu, Jie Kate, Khoshnevis, Naeem, Dominici, Francesca
Spatial confounding poses a significant challenge in scientific studies involving spatial data, where unobserved spatial variables can influence both treatment and outcome, possibly leading to spurious associations. To address this problem, we introd
Externí odkaz:
http://arxiv.org/abs/2312.00710
Motivated by the proliferation of observational datasets and the need to integrate non-randomized evidence with randomized controlled trials, causal inference researchers have recently proposed several new methodologies for combining biased and unbia
Externí odkaz:
http://arxiv.org/abs/2309.06727
Unmeasured confounding bias is among the largest threats to the validity of observational studies. Although sensitivity analyses and various study designs have been proposed to address this issue, they do not leverage the growing availability of auxi
Externí odkaz:
http://arxiv.org/abs/2309.02631
We develop new matching estimators for estimating causal quantile exposure-response functions and quantile exposure effects with continuous treatments. We provide identification results for the parameters of interest and establish the asymptotic prop
Externí odkaz:
http://arxiv.org/abs/2308.01628
In this paper, we undertake a case study in which interest lies in estimating a causal exposure-response function (ERF) for long-term exposure to fine particulate matter (PM$_{2.5}$) and respiratory hospitalizations in socioeconomically disadvantaged
Externí odkaz:
http://arxiv.org/abs/2308.00812
Observational studies are frequently used to estimate the effect of an exposure or treatment on an outcome. To obtain an unbiased estimate of the treatment effect, it is crucial to measure the exposure accurately. A common type of exposure misclassif
Externí odkaz:
http://arxiv.org/abs/2307.02331
Exposure to fine particulate matter ($PM_{2.5}$) poses significant health risks and accurately determining the shape of the relationship between $PM_{2.5}$ and health outcomes has crucial policy ramifications. While various statistical methods exist
Externí odkaz:
http://arxiv.org/abs/2306.03011