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Found 14 papers, 1 papers with code
Discovery of Causal Additive Models in the Presence of Unobserved Variables
In this study, we focus on causal additive models in the presence of unobserved variables.
Causal Discovery with Multi-Domain LiNGAM for Latent Factors
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 of linear non-Gaussian acyclic models in the presence of latent confounders
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.
Analysis of cause-effect inference by comparing regression errors
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.
Combining Linear Non-Gaussian Acyclic Model with Logistic Regression Model for Estimating Causal Structure from Mixed Continuous and Discrete Data
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.
Estimation of interventional effects of features on prediction
The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear.
Error Asymmetry in Causal and Anticausal Regression
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
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
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
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
Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence.
Identifiability of an Integer Modular Acyclic Additive Noise Model and its Causal Structure Discovery
The notion of causality is used in many situations dealing with uncertainty.
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
We consider learning the possible causal direction of two observed variables in the presence of latent confounding variables.
ParceLiNGAM: A causal ordering method robust against latent confounders
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.
