Graph-based Causal Inference for Health Decision Making.pdf

1、Graph-based Causal Inference for Health Decision MakingJundong Li,Assistant ProfessorDepartment of Electrical and Computer Engineering Department of Computer Science,and School of Data Science,University of Virginiahttps:/jundongli.github.io/jundongvirginia.edu 1Graphs are Pervasive in Biology and M

2、edicineFigure reference:Deep Learning for Network Biology-snap.stanford.edu/deepnetbio-ismb-ISMB 2018 2Graph Machine Learning Graph ML has been widely used in different tasks(e.g.,node classification,link prediction,graph classification)Wide a range of applications in the health and biomedical domai

3、n:3classify the function of proteins in the interactomepredict which disease a new molecule might treatunderstand the properties of a particular molecular structureFigures reference:(1)Deep Learning for Network Biology-snap.stanford.edu/deepnetbio-ismb-ISMB 2018Correlation vs.Causation Most of popul

4、ar Graph ML algorithms are built on finding relationships(correlations)from graph data and make use correlations for predictions However,correlation does not imply causation For two correlated events A and B,the possible relations might be:(1)A causes B,(2)B causes A,(3)A and B are consequences of a

5、 common cause,but do not cause each other,etc4Example:does alcohol consumption cause lung cancer?Figures reference:https:/sitn.hms.harvard.edu/flash/2021/when-correlation-does-not-imply-causation-why-your-gut-microbes-may-not-yet-be-a-silver-bullet-to-all-your-problems/Causal Inference on Graphs Cau

6、sal inference studies the causal relations rather than statisticaldependencies between variables Causal effect estimation:assessing the causal effects of a treatment(e.g.,mask wearing)on an outcome(e.g.,disease infection)for one/a group of units Causal effect estimation on graphs Question:Given a co