GraphITE: Estimating Individual Effects of Graph-structured Treatments

no code implementations • 29 Sep 2020 • Shonosuke Harada, Hisashi Kashima

Outcome estimation of treatments for target individuals is an important foundation for decision making based on causal relations.

counterfactual Decision Making +1

Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation

no code implementations • 11 May 2020 • Shonosuke Harada, Hisashi Kashima

Individual treatment effect (ITE) represents the expected improvement in the outcome of taking a particular action to a particular target, and plays important roles in decision making in various domains.

Causal Inference counterfactual +1

Dual Convolutional Neural Network for Graph of Graphs Link Prediction

no code implementations • 4 Oct 2018 • Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima

Graphs are general and powerful data representations which can model complex real-world phenomena, ranging from chemical compounds to social networks; however, effective feature extraction from graphs is not a trivial task, and much work has been done in the field of machine learning and data mining.

Link Prediction