← Authors

Shohei Shimizu

17 papers · 2006–2026 · 6 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🐝 Cross-Pollinator (11) πŸ—ΊοΈ Taxonomy Completionist (11) 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (5) πŸƒ Academic Marathon (18) πŸ”¬ Deep Specialist (15) πŸ† Keyword Champion (4) 🧬 Topic Evolution πŸ”₯ Unstoppable (5) πŸ“ˆ Trend Setter πŸ’Ž Century Club (15) πŸš€ Conference Pioneer

Conferences

JMLR (7) CLEAR (4) AAAI (2) AISTATS (2) IJCAI (1) UAI (1)

Top co-authors

Takashi Nicholas Maeda (4) Yan Zeng (3) Patrik O. Hoyer (3) Takuya Takagi (2) Thong Pham (2) Kun Zhang (2) Kenneth Bollen (2) Takashi Washio (2) Ruichu Cai (2) Aapo HyvΓ€rinen (2)

Keywords

causal discovery (14) causal inference (6) structural equation model (5) linear non-gaussian acyclic model (4) independent component analysis (2) multivariate analysis (2) directed acyclic graph (2) causal additive model (2) non-gaussian distribution (2) unobserved variable (2) causal graph (2) latent confounder (2) time series (2) latent factor (1) structural causal model (1) bayesian network (1) bayesian estimation (1) cause-effect inference (1) linear model (1) latent subspace (1)

Papers

Discovering Linear Non-Gaussian Models for All Categories of Missing Data (Student Abstract) AAAI 2026
I-CAM-UV: Integrating Causal Graphs over Non-Identical Variable Sets Using Causal Additive Models with Unobserved Variables AAAI 2026
Causal-learn: Causal Discovery in Python JMLR 2024
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models CLEAR 2024
Causal Discovery for Non-stationary Non-linear Time Series Data Using Just-In-Time Modeling CLEAR 2023
Python package for causal discovery based on LiNGAM JMLR 2023
A Multivariate Causal Discovery based on Post-Nonlinear Model CLEAR 2022
Causal Discovery for Linear Mixed Data CLEAR 2022
Causal additive models with unobserved variables UAI 2021
Causal Discovery with Multi-Domain LiNGAM for Latent Factors IJCAI 2021
RCD: Repetitive causal discovery of linear non-Gaussian acyclic models with latent confounders AISTATS 2020
Cause-Effect Inference by Comparing Regression Errors AISTATS 2018
Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions JMLR 2017
Bayesian Estimation of Causal Direction in Acyclic Structural Equation Models with Individual-specific Confounder Variables and Non-Gaussian Distributions JMLR 2014
DirectLiNGAM: A Direct Method for Learning a Linear Non-Gaussian Structural Equation Model JMLR 2011
Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity JMLR 2010
A Linear Non-Gaussian Acyclic Model for Causal Discovery JMLR 2006