no code implementations • 24 May 2024 • Claudio Battiloro, Ege Karaismailoğlu, Mauricio Tec, George Dasoulas, Michelle Audirac, Francesca Dominici
This paper introduces E(n)-Equivariant Topological Neural Networks (ETNNs), which are E(n)-equivariant message-passing networks operating on combinatorial complexes, formal objects unifying graphs, hypergraphs, simplicial, path, and cell complexes.
no code implementations • 21 Dec 2023 • Ellen M. Considine, Rachel C. Nethery, Gregory A. Wellenius, Francesca Dominici, Mauricio Tec
First, we introduce a novel RL environment enabling the evaluation of the effectiveness of heat alert policies to reduce heat-related hospitalizations.
1 code implementation • 1 Dec 2023 • Mauricio Tec, Ana Trisovic, Michelle Audirac, Sophie Woodward, Jie Kate Hu, Naeem Khoshnevis, Francesca Dominici
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.
no code implementations • 6 Feb 2023 • Mauricio Tec, Oladimeji Mudele, Kevin Josey, Francesca Dominici
Motivated by a key policy-relevant question in public health, we develop a neural network method and its theoretical underpinnings to estimate SRFs with robustness and efficiency guarantees.
1 code implementation • 18 Sep 2020 • Falco J. Bargagli-Stoffi, Riccardo Cadei, Kwonsang Lee, Francesca Dominici
In health and social sciences, it is critically important to identify subgroups of the study population where there is notable heterogeneity of treatment effects (HTE) with respect to the population average.
1 code implementation • 17 Dec 2018 • Xiao Wu, Fabrizia Mealli, Marianthi-Anna Kioumourtzoglou, Francesca Dominici, Danielle Braun
We apply our proposed method to estimate the average causal exposure-response function between long-term PM$_{2. 5}$ exposure and all-cause mortality among 68. 5 million Medicare enrollees, 2000-2016.
Methodology Applications
no code implementations • NeurIPS 2018 • Fei Jiang, Guosheng Yin, Francesca Dominici
Based on non-local prior distributions, we propose a Bayesian model selection (BMS) procedure for boundary detection in a sequence of data with multiple systematic mean changes.
1 code implementation • 14 Nov 2018 • Rachel C. Nethery, Yue Yang, Anna J. Brown, Francesca Dominici
We overcome the second challenge by developing a Bayesian hierarchical model that borrows information from other sources to impute cancer incidence at the desired finer level of spatial aggregation.
Methodology Applications
no code implementations • 29 May 2018 • M. Benjamin Sabath, Qian Di, Danielle Braun, Joel Schwarz, Francesca Dominici, Christine Choirat
Fine particulate matter (PM$_{2. 5}$) is one of the criteria air pollutants regulated by the Environmental Protection Agency in the United States.
3 code implementations • 4 Jan 2018 • Aaron Fisher, Cynthia Rudin, Francesca Dominici
Expanding on MR, we propose Model Class Reliance (MCR) as the upper and lower bounds on the degree to which any well-performing prediction model within a class may rely on a variable of interest, or set of variables of interest.
Methodology
1 code implementation • 2 Dec 2017 • Xiao Wu, Danielle Braun, Marianthi-Anna Kioumourtzoglou, Christine Choirat, Qian Di, Francesca Dominici
We propose a new approach for estimating causal effects when the exposure is measured with error and confounding adjustment is performed via a generalized propensity score (GPS).
Methodology Applications
1 code implementation • 25 Apr 2017 • Joseph Antonelli, Giovanni Parmigiani, Francesca Dominici
In observational studies, estimation of a causal effect of a treatment on an outcome relies on proper adjustment for confounding.
Methodology