TY - GEN
T1 - The KDD 2022 Workshop on Causal Discovery (CD2022)
AU - Le, Thuc Duy
AU - Liu, Lin
AU - Kiciman, Emre
AU - Triantafyllou, Sofia
AU - Liu, Huan
N1 - Publisher Copyright:
© 2022 Owner/Author.
PY - 2022/8/14
Y1 - 2022/8/14
N2 - Causal relationships have been utilized in almost all disciplines, and the research into causal discovery has attracted a lot of attention in the last few years. Traditionally, causal relationships are identified by making use of interventions or randomized controlled experiments. However, conducting such experiments is often expensive or even impossible due to cost or ethical concerns. Therefore, there has been an increasing interest in discovering causal relationships based on observational data, and in the past few decades, significant contributions have been made to this field by computer scientists. Following the success of CD 2016 - CD 2021, CD 2022 continues to serve as a forum for researchers and practitioners in data mining and other disciplines to share their recent research in causal discovery in their respective fields and to explore the possibility of interdisciplinary collaborations in the study of causality. Based on the platform of KDD, this workshop is especially interested in attracting contributions that link data mining/machine learning research with causal discovery, and solutions to causal discovery in large scale datasets.
AB - Causal relationships have been utilized in almost all disciplines, and the research into causal discovery has attracted a lot of attention in the last few years. Traditionally, causal relationships are identified by making use of interventions or randomized controlled experiments. However, conducting such experiments is often expensive or even impossible due to cost or ethical concerns. Therefore, there has been an increasing interest in discovering causal relationships based on observational data, and in the past few decades, significant contributions have been made to this field by computer scientists. Following the success of CD 2016 - CD 2021, CD 2022 continues to serve as a forum for researchers and practitioners in data mining and other disciplines to share their recent research in causal discovery in their respective fields and to explore the possibility of interdisciplinary collaborations in the study of causality. Based on the platform of KDD, this workshop is especially interested in attracting contributions that link data mining/machine learning research with causal discovery, and solutions to causal discovery in large scale datasets.
KW - causal discovery
KW - causality
KW - data mining
KW - reasoning
UR - http://www.scopus.com/inward/record.url?scp=85137144998&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85137144998&partnerID=8YFLogxK
U2 - 10.1145/3534678.3542890
DO - 10.1145/3534678.3542890
M3 - Conference contribution
AN - SCOPUS:85137144998
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 4884
EP - 4885
BT - KDD 2022 - Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
PB - Association for Computing Machinery
T2 - 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2022
Y2 - 14 August 2022 through 18 August 2022
ER -