Shohei Shimizu
17 papers · 2006–2026 · 6 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
Achievements
Jump to papers ↓+8 more ↓ Show less ↑
πΊοΈ Taxonomy Completionist (10) π Academic Marathon Β· Ultra π Conference Polyglot Β· Silver π Interdisciplinary Bridge Β· Bronze π£ Hot Topic Early Bird
π§
Keyword Pioneer
π
Keyword Champion
(4)
π§¬
Topic Evolution
π¬
Deep Specialist
(15)
π
Conference Pioneer
π
Trend Setter
π
Century Club Β· Bronze
π₯
Unstoppable Β· Bronze
Top co-authors
Research topics
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