55 citations · 208 across the 25 of their papers we have counts for
13 papers · 1 filter
Estimation of interventional effects of features on prediction
Patrick Blöbaum, Shohei Shimizu
The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear. While several studies have focused on developing prediction models…
Learning Instrumental Variables with Non-Gaussianity Assumptions: Theoretical Limitations and Practical Algorithms
Ricardo Silva, Shohei Shimizu
Learning a causal effect from observational data is not straightforward, as this is not possible without further assumptions. If hidden common causes between treatment and outc…
A Bayesian estimation approach to analyze non-Gaussian data-generating processes with latent classes
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. A linea…
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
Takanori Inazumi, Takashi Washio, Shohei Shimizu +3
Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence. Recently, some techniques to di…
Identifiability of an Integer Modular Acyclic Additive Noise Model and its Causal Structure Discovery
Joe Suzuki, Takanori Inazumi, Takashi Washio +1
The notion of causality is used in many situations dealing with uncertainty. We consider the problem whether causality can be identified given data set generated by discrete random…
Bayesian estimation of possible causal direction in the presence of latent confounders using a linear non-Gaussian acyclic structural equation model with individual-specific effects
Shohei Shimizu, Kenneth Bollen
We consider learning the possible causal direction of two observed variables in the presence of latent confounding variables. Several existing methods have been shown to consistent…