5 papers
Differentiable Causal Discovery of Linear Non-Gaussian Acyclic Models Under Unmeasured Confounding
Yoshimitsu Morinishi, Shohei Shimizu
We propose a novel score-based causal discovery method, named ABIC LiNGAM, which extends the linear non-Gaussian acyclic model (LiNGAM) framework to address the challenges of causa…
Causal-discovery-based root-cause analysis and its application in time-series prediction error diagnosis
Hiroshi Yokoyama, Ryusei Shingaki, Kaneharu Nishino +2
Recent rapid advancements of machine learning have greatly enhanced the accuracy of prediction models, but most models remain "black boxes", making prediction error diagnosis chall…
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating
Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka
Explainable artificial intelligence (XAI) has helped elucidate the internal mechanisms of machine learning algorithms, bolstering their reliability by demonstrating the basis of th…
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
Masayuki Takayama, Tadahisa Okuda, Thong Pham +4
In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is important for reasonable causal models reflecting the broad…
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models
Thong Pham, Shohei Shimizu, Hideitsu Hino +1
We consider the problem of estimating the counterfactual joint distribution of multiple quantities of interests (e.g., outcomes) in a multivariate causal model extended from the cl…