no code implementations • 5 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.

1 code implementation • 2 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.

no code implementations • 14 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.

no code implementations • 2 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.

no code implementations • 4 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.

1 code implementation • 31 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.

no code implementations • 4 Jun 2021 • Takashi Nicholas Maeda, Shohei Shimizu

In this study, we focus on causal additive models in the presence of unobserved variables.

no code implementations • 19 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.

no code implementations • 13 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.

no code implementations • 19 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.

no code implementations • 16 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.

1 code implementation • 3 Sep 2017 • Patrick Blöbaum, Shohei Shimizu

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

no code implementations • 11 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.

no code implementations • 9 Nov 2015 • Ricardo Silva, Shohei Shimizu

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

no code implementations • 9 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.

no code implementations • 2 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.

no code implementations • 22 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.

no code implementations • 22 Jan 2014 • Joe Suzuki, Takanori Inazumi, Takashi Washio, Shohei Shimizu

The notion of causality is used in many situations dealing with uncertainty.

no code implementations • 24 Oct 2013 • Shohei Shimizu, Kenneth Bollen

We consider learning the possible causal direction of two observed variables in the presence of latent confounding variables.

no code implementations • 29 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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