54 citations · 67 across the 5 of their papers we have counts for
5 papers
Causal Discovery with Stage Variables for Health Time Series
Bharat Srikishan, Samantha Kleinberg
Using observational data to learn causal relationships is essential when randomized experiments are not possible, such as in healthcare. Discovering causal relationships in time-se…
Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare
Chang Lu, Chandan K. Reddy, Prithwish Chakraborty +2
Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (E…
Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series
Zahra Ebrahimzadeh, Min Zheng, Selcuk Karakas +1
Many real-world time series, such as in health, have changepoints where the system's structure or parameters change. Since changepoints can indicate critical events such as onset o…
Proceedings 3rd Workshop on formal reasoning about Causation, Responsibility, and Explanations in Science and Technology
Bernd Finkbeiner, Samantha Kleinberg
The CREST 2018 workshop is the third in a series of workshops addressing formal approaches to reasoning about causation in systems engineering. The topic of formally identifying th…
The Temporal Logic of Causal Structures
Samantha Kleinberg, Bud Mishra
Computational analysis of time-course data with an underlying causal structure is needed in a variety of domains, including neural spike trains, stock price movements, and gene exp…