A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
GaussDetect-LiNGAM eliminates Gaussianity tests for causal discovery.
Identifies causal effects in LiNGAM models with latent variables.
SVAR-LiNGAM reveals causal order in crypto-asset markets.
Structural equation models and Bayesian networks have been widely used to study causal relationships between continuous variables. Recently, a non-Gaussian method called LiNGAM was proposed to discover such causal models and has been extended in various directions. An important problem with LiNGAM is that the results a…
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 linear non-Gaussian acyclic model (LiNGAM) based on the non-Gaussianity of external influences has been proposed to estimate the data-generating …
New method recovers causal order from dependent data.
New algorithms learn polytree structures from data.
Study explores financial market linkages between Japan and US markets.
A linear non-Gaussian structural equation model called LiNGAM is an identifiable model for exploratory causal analysis. Previous methods estimate a causal ordering of variables and their connection strengths based on a single dataset. However, in many application domains, data are obtained under different conditions, t…
AcceleratedLiNGAM speeds up causal discovery methods for large datasets.
A very important topic in systems biology is developing statistical methods that automatically find causal relations in gene regulatory networks with no prior knowledge of causal connectivity. Many methods have been developed for time series data. However, discovery methods based on steady-state data are often necessar…
New method identifies causal brain connections from fMRI data.
New method estimates causal structure from sparse data.
TSLiNGAM improves causal discovery in heavy-tailed data.
Study uses Wasserstein distance to identify causal orders and unmix sources.
In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully Bayesian hierarchy for …
We consider to learn a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually a…
We consider learning a causal ordering of variables in a linear non-Gaussian acyclic model called LiNGAM. Several existing methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But, the estimation results could be distorted if some assumptions actually a…
New algorithm groups variables by ancestral relationships to improve causal graph estimation accuracy.
In recent years, several methods have been proposed for the discovery of causal structure from non-experimental data (Spirtes et al. 2000; Pearl 2000). Such methods make various assumptions on the data generating process to facilitate its identification from purely observational data. Continuing this line of research, …
New methods discover causal relationships from multiple related data views.
New method discovers causal relationships in sparse linear data.
Proposes MD-LiNA for multi-domain latent factor causal discovery.
Study uses ML and causal analysis to predict student performance factors.
We address the problem of two-variable causal inference without intervention. This task is to infer an existing causal relation between two random variables, i.e. or , from purely observational data. As the option to modify a potential cause is not given in many situations only struc…
Cauchy invariants are now viewed as a powerful tool for investigating the Lagrangian structure of three-dimensional (3D) ideal flow (Frisch & Zheligovsky, Commun. Math. Phys., vol. 326, 2014, pp. 499-505, Podvigina et al., J. Comput. Phys., vol. 306, 2016, pp. 320-342). Looking at such invariants with the modern tools …