TY - GEN
T1 - CauseBox
T2 - 30th ACM International Conference on Information and Knowledge Management, CIKM 2021
AU - Sheth, Paras
AU - Jeong, Ujun
AU - Guo, Ruocheng
AU - Liu, Huan
AU - Selçuk Candan, K.
N1 - Funding Information:
This material is based upon work supported by, or in part by the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF2110030 and W911NF2020124 as well as by the National Science Foundation (NSF) grant 1909555.
Publisher Copyright:
© 2021 ACM.
PY - 2021/10/26
Y1 - 2021/10/26
N2 - Causal inference is a critical task in various fields such as healthcare, economics, marketing and education. Recently, there have been significant advances through the application of machine learning techniques, especially deep neural networks. Unfortunately, to-date many of the proposed methods are evaluated on different (data, software/hardware, hyperparameter) setups and consequently it is nearly impossible to compare the efficacy of the available methods or reproduce results presented in original research manuscripts. In this paper, we propose a causal inference toolbox (CauseBox) that addresses the aforementioned problems. At the time of publication, the toolbox includes seven state of the art causal inference methods and two benchmark datasets. By providing convenient command-line and GUI-based interfaces, the CauseBox toolbox helps researchers fairly compare the state of the art methods in their chosen application context against benchmark datasets. The code is made public at github.com/paras2612/CauseBox.
AB - Causal inference is a critical task in various fields such as healthcare, economics, marketing and education. Recently, there have been significant advances through the application of machine learning techniques, especially deep neural networks. Unfortunately, to-date many of the proposed methods are evaluated on different (data, software/hardware, hyperparameter) setups and consequently it is nearly impossible to compare the efficacy of the available methods or reproduce results presented in original research manuscripts. In this paper, we propose a causal inference toolbox (CauseBox) that addresses the aforementioned problems. At the time of publication, the toolbox includes seven state of the art causal inference methods and two benchmark datasets. By providing convenient command-line and GUI-based interfaces, the CauseBox toolbox helps researchers fairly compare the state of the art methods in their chosen application context against benchmark datasets. The code is made public at github.com/paras2612/CauseBox.
KW - causal inference
KW - deep learning
KW - treatment effect estimation
UR - http://www.scopus.com/inward/record.url?scp=85119177659&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85119177659&partnerID=8YFLogxK
U2 - 10.1145/3459637.3481974
DO - 10.1145/3459637.3481974
M3 - Conference contribution
AN - SCOPUS:85119177659
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 4789
EP - 4793
BT - CIKM 2021 - Proceedings of the 30th ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
Y2 - 1 November 2021 through 5 November 2021
ER -