Search Results for author: Shohei Shimizu

Found 20 papers, 3 papers with code

Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating

no code implementations5 Feb 2024 Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka

However, this assumption complicates the application of such models to real data, given that the causal relationships between features are unknown in most cases.

Causal Discovery counterfactual +2

Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach

1 code implementation2 Feb 2024 Masayuki Takayama, Tadahisa Okuda, Thong Pham, Tatsuyoshi Ikenoue, Shingo Fukuma, Shohei Shimizu, Akiyoshi Sannai

In practical statistical causal discovery (SCD), embedding domain expert knowledge as constraints into the algorithm is widely accepted as significant for creating consistent meaningful causal models, despite the recognized challenges in systematic acquisition of the background knowledge.

Causal Discovery Causal Inference +2

Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data

no code implementations14 Jan 2024 Takashi Nicholas Maeda, Shohei Shimizu

Moreover, by incorporating the prior knowledge that causes precedes their effects in time, we extend the first algorithm to the second method for causal discovery in time series data.

Additive models Causal Discovery +1

Scalable Counterfactual Distribution Estimation in Multivariate Causal Models

no code implementations2 Nov 2023 Thong Pham, Shohei Shimizu, Hideitsu Hino, Tam Le

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 classical difference-in-difference design.


Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United States

no code implementations4 Oct 2023 Yi Jiang, Shohei Shimizu

Our study contributes to a better understanding of the linkages among financial markets in the analyzed data period by supporting the existence of linkages between Japan and the US for the same financial markets and among FX, stock, and bond markets, thus highlighting the importance of leveraging causal discovery methods in the financial domain.

Causal Discovery

Causal-learn: Causal Discovery in Python

1 code implementation31 Jul 2023 Yujia Zheng, Biwei Huang, Wei Chen, Joseph Ramsey, Mingming Gong, Ruichu Cai, Shohei Shimizu, Peter Spirtes, Kun Zhang

Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering.

Causal Discovery

Causal Discovery with Multi-Domain LiNGAM for Latent Factors

no code implementations19 Sep 2020 Yan Zeng, Shohei Shimizu, Ruichu Cai, Feng Xie, Michio Yamamoto, Zhifeng Hao

In this paper, we propose Multi-Domain Linear Non-Gaussian Acyclic Models for Latent Factors (MD-LiNA), where the causal structure among latent factors of interest is shared for all domains, and we provide its identification results.

Causal Discovery

Causal discovery of linear non-Gaussian acyclic models in the presence of latent confounders

no code implementations13 Jan 2020 Takashi Nicholas Maeda, Shohei Shimizu

The results of experimental validation using simulated data and real-world data confirmed that RCD is effective in identifying latent confounders and causal directions between observed variables.

Causal Discovery

Analysis of cause-effect inference by comparing regression errors

no code implementations19 Feb 2018 Patrick Blöbaum, Dominik Janzing, Takashi Washio, Shohei Shimizu, Bernhard Schölkopf

We address the problem of inferring the causal direction between two variables by comparing the least-squares errors of the predictions in both possible directions.

Causal Inference regression

Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data

no code implementations16 Feb 2018 Chao Li, Shohei Shimizu

Most existing causal discovery methods either ignore the discrete data and apply a continuous-valued algorithm or discretize all the continuous data and then apply a discrete Bayesian network approach.

Causal Discovery Model Selection

Estimation of interventional effects of features on prediction

1 code implementation3 Sep 2017 Patrick Blöbaum, Shohei Shimizu

The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear.

Error Asymmetry in Causal and Anticausal Regression

no code implementations11 Oct 2016 Patrick Blöbaum, Takashi Washio, Shohei Shimizu

It is generally difficult to make any statements about the expected prediction error in an univariate setting without further knowledge about how the data were generated.


Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms

no code implementations9 Nov 2015 Ricardo Silva, Shohei Shimizu

Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions.

A direct method for estimating a causal ordering in a linear non-Gaussian acyclic model

no code implementations9 Aug 2014 Shohei Shimizu, Aapo Hyvarinen, Yoshinobu Kawahara

Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables.

A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes

no code implementations2 Aug 2014 Naoki Tanaka, Shohei Shimizu, Takashi Washio

A large amount of observational data has been accumulated in various fields in recent times, and there is a growing need to estimate the generating processes of these data.

Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM

no code implementations22 Jan 2014 Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara

Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence.

Causal Discovery

ParceLiNGAM: A causal ordering method robust against latent confounders

no code implementations29 Mar 2013 Tatsuya Tashiro, Shohei Shimizu, Aapo Hyvarinen, Takashi Washio

In this paper, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders.

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