Search Results for author: Francesca Dominici

Found 11 papers, 6 papers with code

Optimizing Heat Alert Issuance with Reinforcement Learning

no code implementations21 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.

Data Augmentation Decision Making +2

SpaCE: The Spatial Confounding Environment

1 code implementation1 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.

Causal Inference

Causal Estimation of Exposure Shifts with Neural Networks: Evaluating the Health Benefits of Stricter Air Quality Standards in the US

no code implementations6 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.

Causal Inference

Causal Rule Ensemble: Interpretable Discovery and Inference of Heterogeneous Treatment Effects

no code implementations18 Sep 2020 Falco J. Bargagli-Stoffi, Riccardo Cadei, Kwonsang Lee, Francesca Dominici

Estimation of subgroup-specific causal effects is performed via a two-stage approach for which we provide theoretical guarantees.

Causal Inference Epidemiology

Matching on Generalized Propensity Scores with Continuous Exposures

1 code implementation17 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

Bayesian Model Selection Approach to Boundary Detection with Non-Local Priors

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.

Boundary Detection Change Point Detection +1

A causal inference framework for cancer cluster investigations using publicly available data

1 code implementation14 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

airpred: A Flexible R Package Implementing Methods for Predicting Air Pollution

no code implementations29 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.

Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective

3 code implementations4 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

Causal inference in the context of an error prone exposure: air pollution and mortality

1 code implementation2 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

High-dimensional confounding adjustment using continuous spike and slab priors

1 code implementation25 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

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