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Found 20 papers, 3 papers with code
Causal-learn: Causal Discovery in Python
Causal discovery aims at revealing causal relations from observational data, which is a fundamental task in science and engineering.
Estimation of interventional effects of features on prediction
The interpretability of prediction mechanisms with respect to the underlying prediction problem is often unclear.
Integrating Large Language Models in Causal Discovery: A Statistical Causal Approach
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.
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.
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.
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.
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.
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.
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.
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.
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.
Linkages among the Foreign Exchange, Stock, and Bond Markets in Japan and the United States
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.
Scalable Counterfactual Distribution Estimation in Multivariate Causal Models
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.
Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data
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.
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating
However, this assumption complicates the application of such models to real data, given that the causal relationships between features are unknown in most cases.
