TY - JOUR
T1 - Automated Identification of Causal Moderators in Time-Series Data
AU - Zheng, Min
AU - Claassen, Jan
AU - Kleinberg, Samantha
N1 - Publisher Copyright:
© 2018 PMLR. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Causal inference is often taken to mean finding links between individual variables. However in many real-world cases, such as in biological systems, relationships are more complex, with groups of factors needed to produce an effect, or some factors only modifying other relationships rather than producing outcomes alone. For instance, weight may alter the efficacy of a drug without causing side effects itself. Such moderating factors may change the timing, intensity, or probability of a causal relationship. Distinguishing moderators from genuine causes can lead to more effective medical interventions, and better strategies for bringing about a desired effect, since a moderator alone is ineffective. However, there have not yet been algorithms to automatically infer moderators in a large-scale automated way, and they cannot be easily read off from causal graphs. We introduce a set of temporal logic rules to automatically identify the asymmetric roles of causes and moderators in a computationally efficient manner. Experiments on simulated data demonstrate that even in challenging cases we can find moderators and avoid confounding, and on real neurological ICU data we show how the approach can find more descriptive and meaningful relationships than the state of the art.
AB - Causal inference is often taken to mean finding links between individual variables. However in many real-world cases, such as in biological systems, relationships are more complex, with groups of factors needed to produce an effect, or some factors only modifying other relationships rather than producing outcomes alone. For instance, weight may alter the efficacy of a drug without causing side effects itself. Such moderating factors may change the timing, intensity, or probability of a causal relationship. Distinguishing moderators from genuine causes can lead to more effective medical interventions, and better strategies for bringing about a desired effect, since a moderator alone is ineffective. However, there have not yet been algorithms to automatically infer moderators in a large-scale automated way, and they cannot be easily read off from causal graphs. We introduce a set of temporal logic rules to automatically identify the asymmetric roles of causes and moderators in a computationally efficient manner. Experiments on simulated data demonstrate that even in challenging cases we can find moderators and avoid confounding, and on real neurological ICU data we show how the approach can find more descriptive and meaningful relationships than the state of the art.
KW - causality
KW - health informatics
KW - time series
UR - http://www.scopus.com/inward/record.url?scp=85123418974&partnerID=8YFLogxK
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M3 - Conference article
AN - SCOPUS:85123418974
VL - 92
SP - 4
EP - 22
JO - Proceedings of Machine Learning Research
JF - Proceedings of Machine Learning Research
T2 - 2018 ACM SIGKDD Workshop on Causal Discovery, CD 2018
Y2 - 20 August 2018
ER -