1 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2025★ 1 cited
A Model of Causal Explanation on Neural Networks for Tabular Data
Takashi Isozaki, Masahiro Yamamoto, Atsushi Noda
The problem of explaining the results produced by machine learning methods continues to attract attention. Neural network (NN) models, along with gradient boosting machines, are ex…
cs.LG2025★ 1 cited
Practically Effective Adjustment Variable Selection in Causal Inference
Atsushi Noda, Takashi Isozaki
In the estimation of causal effects, one common method for removing the influence of confounders is to adjust the variables that satisfy the back-door criterion. However, it is not…