Exposure Data

Exposure data research focuses on accurately measuring and analyzing the influence of various exposures—ranging from screen time in children to air pollution and AI-driven job displacement—on specific outcomes. Current research emphasizes developing robust methods for data acquisition, such as novel sensor informatics and vision-language models, and for causal inference, employing techniques like local causal discovery and targeted regularization within neural networks to account for confounding factors. These advancements are crucial for improving the accuracy and efficiency of epidemiological studies, informing public health interventions, and understanding the societal impact of technological advancements.

Papers

October 2, 2024

Enhancing Screen Time Identification in Children with a Multi-View Vision Language Model and Screen Time Tracker
Xinlong Hou, Sen Shen, Xueshen Li, Xinran Gao, Ziyi Huang, Steven J. Holiday, Matthew R. Cribbet, Susan W. White, Edward Sazonov, Yu Gan
Vision Language Model Nine Year Old Child Wearable Sensor New Tracker Screen Content Exposure Data

October 25, 2023

Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs
Jacqueline Maasch, Weishen Pan, Shantanu Gupta, Volodymyr Kuleshov, Kyra Gan, Fei Wang
Causal Inference Causal Discovery Latent Confounding Exposure Data Local Causal Discovery

August 17, 2023

Large Language Models at Work in China's Labor Market
Qin Chen, Jinfeng Ge, Huaqing Xie, Xingcheng Xu, Yanqing Yang
Rural China Labor Market Macroeconomic Model Exposure Data

August 5, 2023

Robust estimation of exposure ratios in multi-exposure image stacks
Param Hanji, Rafał K. Mantiuk
High Dynamic Range Robust Estimation Exposure Estimation Exposure Data Exposure Time

April 7, 2023

On-site Noise Exposure technique for noise-robust machine fault classification
Wonjun Yi, Jung-Woo Choi
Industrial Disturbing Noise Noisy Data Fault Diagnosis Machine Condition Monitoring Environmental Noise Machine Sound Exposure Data

March 8, 2023

Automatic Debiased Learning from Positive, Unlabeled, and Exposure Data
Masahiro Kato, Shuting Wu, Kodai Kureishi, Shota Yasui
Binary Classification Selection Bias Positive Emotional Language Debiasing Framework Model Debiasing Positive Label Pu Learning Exposure Data

February 6, 2023

Causal Estimation of Exposure Shifts with Neural Networks
Mauricio Tec, Kevin Josey, Oladimeji Mudele, Francesca Dominici
Neural Network Causal Inference Causal Effect Causal Effect Estimation Air Pollution Frequency Response Exposure Data Robust Causal

January 16, 2023

Sensor data-driven analysis for identification of causal relationships between exposure to air pollution and respiratory rate in asthmatics
D K Arvind, S Maiya
Data Driven Causal Discovery Causal Effect Causal Relationship Air Pollution Respiratory Rate Exposure Data Improved Asthma Control

August 15, 2022

Debiased Recommendation with Neural Stratification
Quanyu Dai, Zhenhua Dong, Xu Chen
Recommendation Model Debiased Recommendation Inverse Propensity Recommender Model Exposure Data

May 27, 2022

Average Adjusted Association: Efficient Estimation with High Dimensional Confounders
Sung Jae Jun, Sokbae Lee
Time Varying Text Based Confounders Efficient Estimation Association Test Equal Exposure Corridor Exposure Data