scholarly journals Causal Discovery with Multi-Domain LiNGAM for Latent Factors

2021 ◽  
Author(s):  
Yan Zeng ◽  
Shohei Shimizu ◽  
Ruichu Cai ◽  
Feng Xie ◽  
Michio Yamamoto ◽  
...  
Keyword(s):  
Causal Structure ◽  
Score Function ◽  
Factor Loading ◽  
Real World Data ◽  
Latent Factors ◽  
Two Phase ◽  
Loading Matrix ◽  
Non Gaussian ◽  
Causal Structures

Discovering causal structures among latent factors from observed data is a particularly challenging problem. Despite some efforts for this problem, existing methods focus on the single-domain data only. 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. The model enriches the causal representation for multi-domain data. We propose an integrated two-phase algorithm to estimate the model. In particular, we first locate the latent factors and estimate the factor loading matrix. Then to uncover the causal structure among shared latent factors of interest, we derive a score function based on the characterization of independence relations between external influences and the dependence relations between multi-domain latent factors and latent factors of interest. We show that the proposed method provides locally consistent estimators. Experimental results on both synthetic and real-world data demonstrate the efficacy and robustness of our approach.

scholarly journals An estimation of causal structure based on Latent LiNGAM for mixed data

2019 ◽  
Vol 47 (1) ◽  
pp. 105-121 ◽  
Author(s):  
Mako Yamayoshi ◽  
Jun Tsuchida ◽  
Hiroshi Yadohisa
Keyword(s):  
Latent Variable ◽  
Structural Equation ◽  
Numerical Study ◽  
Causal Structure ◽  
Estimation Method ◽  
Mixed Data ◽  
Equation Modeling ◽  
Discrete Variables ◽  
Real World Data ◽  
Non Gaussian

Abstract The linear non-gaussian acyclic model (LiNGAM) has been proposed as a method for estimating causal structures using structural equation modeling (SEM). LiNGAM is useful as an exploratory estimation method for a causal structure. However, the assumptions that all observed variables in LiNGAM are continuous is not applicable in case of mixed data (i.e., when discrete variables are also included in the dataset). Therefore, we propose the Latent LiNGAM (L-LiNGAM), where each variable corresponds to a continuous latent variable and is observed as data through transformation via a link function. In the numerical study, when mixing discrete variables, the estimation of causal structure using L-LiNGAM is proven useful in terms of sum of squared error and path recovery. Moreover, from real-world data applications, the causal structure estimated by L-LiNGAM is shown to be the best for evaluation under SEM. The model fit is also superior to that of existing methods.

scholarly journals Causal Discovery from Multiple Data Sets with Non-Identical Variable Sets

2020 ◽  
Vol 34 (06) ◽  
pp. 10153-10161
Author(s):  
Biwei Huang ◽  
Kun Zhang ◽  
Mingming Gong ◽  
Clark Glymour
Keyword(s):  
Causal Structure ◽  
Estimation Procedure ◽  
Causal Discovery ◽  
Data Sets ◽  
Real World Data ◽  
Data Set ◽  
Multiple Data ◽  
Multiple Data Sets ◽  
Distribution Shifts ◽  
Non Gaussian

A number of approaches to causal discovery assume that there are no hidden confounders and are designed to learn a fixed causal model from a single data set. Over the last decade, with closer cooperation across laboratories, we are able to accumulate more variables and data for analysis, while each lab may only measure a subset of them, due to technical constraints or to save time and cost. This raises a question of how to handle causal discovery from multiple data sets with non-identical variable sets, and at the same time, it would be interesting to see how more recorded variables can help to mitigate the confounding problem. In this paper, we propose a principled method to uniquely identify causal relationships over the integrated set of variables from multiple data sets, in linear, non-Gaussian cases. The proposed method also allows distribution shifts across data sets. Theoretically, we show that the causal structure over the integrated set of variables is identifiable under testable conditions. Furthermore, we present two types of approaches to parameter estimation: one is based on maximum likelihood, and the other is likelihood free and leverages generative adversarial nets to improve scalability of the estimation procedure. Experimental results on various synthetic and real-world data sets are presented to demonstrate the efficacy of our methods.

Characterization of flow regimes in two phase flow using wavelet analysis

2018 ◽  
Author(s):  
Munzarin Morshed ◽  
Syed Imtiaz ◽  
Mohammad Aziz Rahman
Keyword(s):  
Wavelet Analysis ◽  
Two Phase Flow ◽  
Flow Regimes ◽  
Phase Flow ◽  
Two Phase

Small angle X-ray scattering study of porosity variation in a styrene-divinylbenzene copolymer

1981 ◽  
Vol 46 (7) ◽  
pp. 1675-1681 ◽  
Author(s):  
Josef Baldrian ◽  
Božena N. Kolarz ◽  
Henrik Galina
Keyword(s):  
Small Angle ◽  
Structural Parameters ◽  
Two Phase ◽  
X Ray ◽  
Porosity Variation ◽  
Swelling Agent ◽  
X Ray Scattering ◽  
Styrene Divinylbenzene ◽  
Ray Scattering

Porosity variations induced by swelling agent exchange were studied in a styrene-divinylbenzene copolymer. Standard methods were used in the characterization of copolymer porosity in the dry state and the results were compared with related structural parameters derived from small angle X-ray scattering (SAXS) measurements as developed for the characterization of two-phase systems. The SAXS method was also used for porosity determination in swollen samples. The differences in the porosity of dry samples were found to be an effect of the drying process, while in the swollen state the sample swells and deswells isotropically.

Characterization of alcohol/salt aqueous two-phase system for optimal separation of gallic acids

2021 ◽  
Vol 131 (5) ◽  
pp. 537-542
Author(s):  
Hui Suan Ng ◽  
Phei Er Kee ◽  
Hip Seng Yim ◽  
Joo Shun Tan ◽  
Yin Hui Chow ◽  
...  
Keyword(s):  
Phase System ◽  
Two Phase ◽  
Aqueous Two Phase System ◽  
Optimal Separation

scholarly journals Cyclic quantum causal models

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jonathan Barrett ◽  
Robin Lorenz ◽  
Ognyan Oreshkov
Keyword(s):  
Causal Reasoning ◽  
Causal Structure ◽  
Bell Inequalities ◽  
Quantum Correlations ◽  
Quantum Systems ◽  
Causal Models ◽  
Causal Order ◽  
Quantum Processes ◽  
Causal Explanations ◽  
Causal Structures

AbstractCausal reasoning is essential to science, yet quantum theory challenges it. Quantum correlations violating Bell inequalities defy satisfactory causal explanations within the framework of classical causal models. What is more, a theory encompassing quantum systems and gravity is expected to allow causally nonseparable processes featuring operations in indefinite causal order, defying that events be causally ordered at all. The first challenge has been addressed through the recent development of intrinsically quantum causal models, allowing causal explanations of quantum processes – provided they admit a definite causal order, i.e. have an acyclic causal structure. This work addresses causally nonseparable processes and offers a causal perspective on them through extending quantum causal models to cyclic causal structures. Among other applications of the approach, it is shown that all unitarily extendible bipartite processes are causally separable and that for unitary processes, causal nonseparability and cyclicity of their causal structure are equivalent.

scholarly journals The Inflation Technique Completely Solves the Causal Compatibility Problem

2020 ◽  
Vol 8 (1) ◽  
pp. 70-91 ◽  
Author(s):  
Miguel Navascués ◽  
Elie Wolfe
Keyword(s):  
Linear Programming ◽  
Latent Variables ◽  
Causal Explanation ◽  
Causal Structure ◽  
Categorical Variables ◽  
False Negatives ◽  
The Given ◽  
Causal Structures ◽  
Compatibility Question ◽  
Structure Graph

AbstractThe causal compatibility question asks whether a given causal structure graph — possibly involving latent variables — constitutes a genuinely plausible causal explanation for a given probability distribution over the graph’s observed categorical variables. Algorithms predicated on merely necessary constraints for causal compatibility typically suffer from false negatives, i.e. they admit incompatible distributions as apparently compatible with the given graph. In 10.1515/jci-2017-0020, one of us introduced the inflation technique for formulating useful relaxations of the causal compatibility problem in terms of linear programming. In this work, we develop a formal hierarchy of such causal compatibility relaxations. We prove that inflation is asymptotically tight, i.e., that the hierarchy converges to a zero-error test for causal compatibility. In this sense, the inflation technique fulfills a longstanding desideratum in the field of causal inference. We quantify the rate of convergence by showing that any distribution which passes the nth-order inflation test must be $\begin{array}{} \displaystyle {O}{\left(n^{{{-}{1}}/{2}}\right)} \end{array}$-close in Euclidean norm to some distribution genuinely compatible with the given causal structure. Furthermore, we show that for many causal structures, the (unrelaxed) causal compatibility problem is faithfully formulated already by either the first or second order inflation test.

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