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Found 5 papers, 1 papers with code
Causal Discovery with Stage Variables for Health Time Series
Using observational data to learn causal relationships is essential when randomized experiments are not possible, such as in healthcare.
Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare
Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients.
Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series
Many real-world time series, such as in health, have changepoints where the system's structure or parameters change.
Pyramid Recurrent Neural Networks for Multi-Scale Change-Point Detection
To address this, we show how changepoint detection can be treated as a supervised learning problem, and propose a new deep neural network architecture that can efficiently identify both abrupt and gradual changes at multiple scales.
Proceedings 3rd Workshop on formal reasoning about Causation, Responsibility, and Explanations in Science and Technology
A further objective is to link to the foundations of causal reasoning in the philosophy of sciences and to causal reasoning performed in other areas of computer science, engineering, and beyond.
