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pro vyhledávání: '"Dominici, Francesca"'
Studies investigating the causal effects of spatially varying exposures on health$\unicode{x2013}$such as air pollution, green space, or crime$\unicode{x2013}$often rely on observational and spatially indexed data. A prevalent challenge is unmeasured
Externí odkaz:
http://arxiv.org/abs/2411.10381
Autor:
Guidi, Gianluca, Dominici, Francesca, Gilmour, Jonathan, Butler, Kevin, Bell, Eric, Delaney, Scott, Bargagli-Stoffi, Falco J.
The rapid proliferation of data centers in the US - driven partly by the adoption of artificial intelligence - has set off alarm bells about the industry's environmental impact. We compiled detailed information on 2,132 US data centers operating betw
Externí odkaz:
http://arxiv.org/abs/2411.09786
The electric power sector is one of the largest contributors to greenhouse gas emissions in the world. In recent years, there has been an unprecedented increase in electricity demand driven by the so-called Artificial Intelligence (AI) revolution. Al
Externí odkaz:
http://arxiv.org/abs/2410.09029
Topological Deep Learning (TDL) has emerged as a paradigm to process and learn from signals defined on higher-order combinatorial topological spaces, such as simplicial or cell complexes. Although many complex systems have an asymmetric relational st
Externí odkaz:
http://arxiv.org/abs/2409.08389
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 threatens the validity of observational studies. While sensitivity analyses and study designs have been proposed to address this issue, they often overlook the growing availability of auxiliary data. Using negative control
Externí odkaz:
http://arxiv.org/abs/2309.02631