On-device research index

arXiv research

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.

169,051 papers · 148 categories

Trend · papers per month

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48 results for “Gaussian SEMs”

Identifiability of Gaussian Structural Equation Models with Homogeneous and Heterogeneous Error Variancesstat.ML

New method identifies Gaussian SEMs with varying error variances.

problem Identify Gaussian SEMs with both homogeneous and heterogeneous error variances.

method Exploits error variances and edge weights; provides a statistically consistent and feasible structure learning algorithm.

result Proves identifiability of Gaussian SEMs with both homogeneous and heterogeneous unknown error variances.

structural equation models identifiability Gaussian SEMs error variances edge weights

2019-01-29abs ↗pdf ↗

Multi-View Causal Discovery without Non-Gaussianity: Identifiability and Algorithmscs.LG

New methods discover causal relationships from multiple related data views.

problem Causal discovery from non-Gaussian data.

method Multi-view linear Structural Equation Model (SEM) with weak assumptions.

result Identifiability of acyclic SEMs and successful causal graph estimation.

causal discovery multi-view data linear SEM non-Gaussianity structural equation model

2025-02-27abs ↗pdf ↗

Consistent Bayesian causal discovery for structural equation models with equal error variancesmath.ST

Bayesian method recovers causal structure in SEMs with equal error variances.

problem Recovering causal structure in SEMs with equal error variances.

method Bayesian DAG selection method using g-priors and the key property of minimum expected squared errors.

result The method consistently recovers the true graph without additional distributional assumptions.

Bayesian causal discovery structural equation models equal error variances consistent recovery g-priors

2025-09-18abs ↗pdf ↗

A Theoretical and Experimental Comparison of the EM and SEM Algorithmcs.LG

In this paper we provide a new analysis of the SEM algorithm. Unlike previous work, we focus on the analysis of a single run of the algorithm. First, we discuss the algorithm for general mixture distributions. Second, we consider Gaussian mixture models and show that with high probability the update equations of the EM…

2013-10-18abs ↗pdf ↗

Causal network learning with non-invertible functional relationshipsstat.ML

Paper develops a method to learn causal networks with non-invertible functions.

problem Identifying causal relationships from observational data with non-invertible functional relationships.

method Proposes a test for non-invertible bivariate causal models and develops a method to incorporate this test in structure learning of DAGs.

result Our algorithms outperform existing DAG learning methods in identifying causal graphical structures.

causal network learning non-invertible functions nonlinear SEMs structure learning DAGs

2020-04-20abs ↗pdf ↗

Learning Mixtures of Unknown Causal Interventionsstat.ML

New method disentangles mixed interventional and observational data in SEMs.

problem Learning causal relationships from mixed interventional and observational data.

method Developed a method to disentangle mixed interventional and observational data in linear SEMs with Gaussian noise.

result The method can identify causal graphs up to their interventional Markov Equivalence Class.

Causal Discovery Interventional Data Linear SEMs Gaussian Noise Markov Equivalence

2024-10-31abs ↗pdf ↗

Sinkhorn EM: An Expectation-Maximization algorithm based on entropic optimal transportstat.ML

sEM uses optimal transport to improve EM algorithm for better convergence and avoiding local optima.

problem Improving the EM algorithm for better convergence and avoiding local optima.

method sEM uses entropic optimal transport to compute responsibilities in the expectation step, leading to better global convergence guarantees and avoiding local optima.

result sEM learns cell labels significantly better than other approaches, improving convergence and avoiding local optima.

EM algorithm optimal transport sinkhorn divergence convergence local optima

2020-06-30abs ↗pdf ↗

Learning Bidirectional Causal Interactions with Heteroscedastic Neural Networksstat.ML

SEM-DNN learns reciprocal interactions from observational data without external instruments.

problem Estimating bidirectional interactions from endogenous data.

method Heteroscedastic neural simultaneous-equation estimator (SEM-DNN) that learns reciprocal structural interactions.

result SEM-DNN recovers structural effects more reliably than other methods under increasing information.

causal inference neural networks heteroscedasticity simultaneous equations endogeneity

2026-07-24abs ↗pdf ↗

Learning linear structural equation models in polynomial time and sample complexitycs.LG

The problem of learning structural equation models (SEMs) from data is a fundamental problem in causal inference. We develop a new algorithm --- which is computationally and statistically efficient and works in the high-dimensional regime --- for learning linear SEMs from purely observational data with arbitrary noise …

2017-07-15abs ↗pdf ↗

From Structural Equation Modelling to Double Machine Learning: Robustness Analysis for Survey-Based Researchcs.LG

Study combines SEM, OLS, and DML for robustness checks in survey-based research.

problem Stability of SEM findings under alternative estimation frameworks.

method Staged robustness analysis framework connecting SEM, OLS, and DML.

result Identifies stable and unstable relationships across SEM, OLS, and DML checks.

Structural Equation Modeling Double Machine Learning Survey Research Robustness Analysis Latent Constructs

2026-07-01abs ↗pdf ↗

Analyzing the Interpretability Robustness of Self-Explaining Modelscs.LG

SEMs fail to provide robust explanations to adversarial inputs.

problem Lack of robustness in interpretability of self-explaining models.

method Evaluation of current SEMs and creation of adversarial inputs.

result Adversarial inputs can cause significant changes in explanations without affecting model outputs.

self-explaining models interpretability robustness adversarial inputs model robustness

2019-05-27abs ↗pdf ↗

Direct Learning with Guarantees of the Difference DAG Between Structural Equation Modelsstat.ME

Paper directly estimates structural difference between SEMs from samples.

problem Estimating change in causal relationships between two conditions.

method Principled algorithm that recovers structural difference SEM in O(d^2 log p) samples.

result Method outperforms state-of-the-art and validates usefulness in medical domain.

structural equation models causal inference difference estimation sample complexity medical domain

2019-06-28abs ↗pdf ↗

Covariance-Based Structural Equation Modeling in Small-Sample Settings with

p>n

cs.LG

Novel approach for SEM in small samples with $p>n$ .

problem Small sample size and

p>n

issues in factor-based SEM.

method Reformulates covariance structure into self-covariance and cross-covariance, defines a feasible set with relative error constraint.

result Improved stability and directional information in small-sample settings.

covariance-based SEM small sample p>n likelihood-based estimation feasible set

2026-04-18abs ↗pdf ↗

Kernel-Based Structural Equation Models for Topology Identification of Directed Networksstat.ML

Structural equation models (SEMs) have been widely adopted for inference of causal interactions in complex networks. Recent examples include unveiling topologies of hidden causal networks over which processes such as spreading diseases, or rumors propagate. The appeal of SEMs in these settings stems from their simplici…

2016-05-10abs ↗pdf ↗

PAC Generalization via Invariant Representationscs.LG

The paper tackles generalization in machine learning by finding invariant representations of data.

problem Obtaining robust models that generalize well across different training environments.

method The paper introduces the concept of

ε

-approximate invariance to study the robustness of models to unseen SEMs.

result The paper provides finite-sample out-of-distribution generalization guarantees for approximate invariance in linear SEMs.

invariant representations generalization machine learning structural equation models PAC learning

2022-05-30abs ↗pdf ↗

Sub-Riemannian Snakes on the Projective Line Bundle with Applications to Segmentation of SEM Imagesmath.DG

A new snake model improves segmentation of SEM images.

problem Efficiently segmenting overlapping electronic structures in SEM images.

method Geodesic tracking on projective line bundle with a geometric criterion for switching between fast spatial snakes and minimizing geodesics.

result Improved robust and automatic segmentation of overlapping electronic structures in SEM images.

SEM images segmentation geodesic tracking projective line bundle snake model

2026-04-13abs ↗pdf ↗

SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation Modelingstat.ML

SLEM uses machine learning to improve causal inference from observational data.

problem Improving causal inference from observational data using non-linear relationships.

method Super Learner Equation Modeling integrating machine learning ensembles.

result SLEM provides consistent and unbiased estimates of causal effects.

causal inference machine learning path modeling structural equation modeling Super Learner

2023-08-08abs ↗pdf ↗

Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approachstat.ML

New neural approach for estimating SEMs with provable convergence.

problem Estimating structural parameters in SEMs.

method Formulated as a min-max game with neural networks, learned using stochastic gradient descent.

result Global convergence in overparametrized regime, improving state-of-the-art.

structural equation models neural networks min-max game global convergence overparametrization

2020-07-02abs ↗pdf ↗

A Semantic-Sampling Framework for Evaluating Calibration in Open-Ended Question Answeringcs.CL

A new framework evaluates LLM calibration in open-ended QA.

problem Evaluating LLM calibration in open-ended QA settings.

method Sem-ECE framework: sampling answers, grouping by semantic classes, and using frequencies as confidence.

result Sem-ECE estimators are unbiased and Sem

_2

achieves smaller calibration error.

LLM calibration open-ended QA semantic sampling expected calibration error logit-based metrics

2026-05-08abs ↗pdf ↗

Iterative Identification Closure: Amplifying Causal Identifiability in Linear SEMsstat.ML

IIC decouples causal identification into two phases, significantly reducing the HTC gap in linear SEMs.

problem Determining causal effect coefficients in linear SEMs with latent confounders using the Half-Trek Criterion (HTC) leaves a gap of inconclusive causal effects.

method Iterative Identification Closure (IIC) framework that decouples causal identification into two phases: a seed function S_0 and Reduced HTC propagation.

result IIC strictly subsumes both HTC and ancestor decomposition, reducing the HTC gap by over 80% with combined seeds.

causal inference linear structural equation models latent confounders Half-Trek Criterion seed function

2026-04-10abs ↗pdf ↗

Causal Consistency of Structural Equation Modelsstat.ML

Complex systems can be modelled at various levels of detail. Ideally, causal models of the same system should be consistent with one another in the sense that they agree in their predictions of the effects of interventions. We formalise this notion of consistency in the case of Structural Equation Models (SEMs) by intr…

2017-07-04abs ↗pdf ↗

BCD Nets: Scalable Variational Approaches for Bayesian Causal Discoverycs.LG

BCD Nets use variational inference to estimate DAGs with uncertainty.

problem Uncertainty in inferring causal graphs from limited data.

method Variational inference framework for Bayesian DAG estimation.

result BCD Nets outperform maximum-likelihood methods in low data regimes.

Bayesian causal discovery variational inference structural equation models discrete optimization low data regimes

2021-12-06abs ↗pdf ↗

Boosting Causal Additive Modelsstat.ML

Boosting method for causal SEMs from observational data.

problem Learning causal order among variables from observational data.

method Boosting-based approach with early stopping and component-wise gradient descent.

result Boosting with early stopping consistently favors the true causal ordering.

causal inference structural equation models observational data boosting gradient descent

2024-01-12abs ↗pdf ↗

Staged Mixture Modelling and Boostingcs.LG

In this paper, we introduce and evaluate a data-driven staged mixture modeling technique for building density, regression, and classification models. Our basic approach is to sequentially add components to a finite mixture model using the structural expectation maximization (SEM) algorithm. We show that our technique i…

2012-12-12abs ↗pdf ↗

Masked Gradient-Based Causal Structure Learningcs.LG

Paper learns causal structures from data using gradient-based optimization.

problem Learning causal structures from observational data.

method Reformulates SEM with binary adjacency matrix and uses gradient-based optimization.

result Gradient-based method efficiently identifies causal structures from data.

causal inference gradient optimization structural equation model binary adjacency matrix

2019-10-18abs ↗pdf ↗

Learning Linear Polytree Structural Equation Modelsstat.ML

Researchers study learning polytree graphs from linear SEMs with exact recovery conditions.

problem Learning polytree graphs from linear SEMs with exact recovery conditions.

method Study Gaussian polytree models, derive sufficient and necessary conditions for sample sizes, and establish estimation error bounds.

result Sharp characterization of difficulty with matching sufficient and necessary conditions.

polytree structural equation model Chow-Liu algorithm CPDAG inverse correlation matrix

2021-07-22abs ↗pdf ↗

Consistent DAG selection for Bayesian causal discovery under general error distributionsmath.ST

Bayesian method identifies causal DAG structure from non-Gaussian errors.

problem Learning causal structure from non-Gaussian errors in Bayesian networks.

method Bayesian hierarchical model with DAG prior for non-Gaussian errors.

result Posterior DAG selection consistency achieved under mild assumptions.

Bayesian causal discovery non-Gaussian errors DAG selection scale mixture of Gaussian

2025-08-01abs ↗pdf ↗

Loss convergence in a causal Bayesian neural network of retail firm performancestat.ML

Improved neural network convergence with causal Bayesian modeling in retail performance.

problem Improving neural network convergence in retail performance models.

method Causal Bayesian neural network implementation, removal of weakest SEM path, Flipout layers, Vadam optimizer.

result Neural network convergence improved with removal of the weakest SEM path.

neural networks causal modeling retail performance convergence Flipout layers

2020-08-29abs ↗pdf ↗

Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networksstat.ML

Directed networks are pervasive both in nature and engineered systems, often underlying the complex behavior observed in biological systems, microblogs and social interactions over the web, as well as global financial markets. Since their structures are often unobservable, in order to facilitate network analytics, one …

2016-10-26abs ↗pdf ↗

A New Statistical Framework for Genetic Pleiotropic Analysis of High Dimensional Phenotype Datastat.ML

The widely used genetic pleiotropic analysis of multiple phenotypes are often designed for examining the relationship between common variants and a few phenotypes. They are not suited for both high dimensional phenotypes and high dimensional genotype (next-generation sequencing) data. To overcome these limitations, we …

2015-12-03abs ↗pdf ↗

Gradient-based training of Gaussian Mixture Models for High-Dimensional Streaming Datacs.LG

Efficiently trains GMMs for streaming data with SGD, addressing local optima and numerical instabilities.

problem Local optima and numerical instabilities in training GMMs for high-dimensional streaming data.

method Stochastic Gradient Descent (SGD) with adaptive annealing and exponential-free approximation.

result SGD approach trains GMMs without k-means initialization and outperforms sEM for high-dimensional data.

Gaussian Mixture Models Streaming Data Stochastic Gradient Descent Local Optima Numerical Instabilities

2019-12-18abs ↗pdf ↗

Robust Causal Bandits for Linear Modelsstat.ML

This paper addresses robust CBs for linear SEMs with model fluctuations.

problem Designing interventions in causal systems with linear SEMs that are robust to model fluctuations.

method Develops a robust CB algorithm and analyzes its regret under model deviation.

result The proposed algorithm achieves nearly optimal

ilde{\mathcal{O}}(\sqrt{T})

regret when

C

o(\sqrt{T})

and maintains sub-linear regret for a broader range of

C

causal bandits linear SEMs model fluctuations cumulative regret sub-linear regret

2023-10-30abs ↗pdf ↗

Learning Sparse Nonparametric DAGsstat.ML

Framework for learning sparse DAGs from data.

problem Learning sparse directed acyclic graphs (DAGs) from data.

method Algebraic characterization of DAGs extended to nonparametric SEM, continuous optimization problem.

result General framework applicable to various nonparametric and semiparametric models.

DAGs nonparametric sparse structural equation models continuous optimization

2019-09-29abs ↗pdf ↗

Causal Bandits for Linear Structural Equation Modelsstat.ML

The paper tackles causal bandits for SEMs, proposing algorithms that avoid estimating $2^N$ reward distributions.

problem Designing an optimal sequence of interventions in causal graphical models to minimize cumulative regret.

method Proposes two algorithms for causal bandits for linear structural equation models (SEMs), avoiding the estimation of

2^N

reward distributions.

result Cumulative regrets scale as

ilde{\cal O} (d^{L+\frac{1}{2}} \sqrt{NT})

under bounded noise and parameter space.

causal bandits linear structural equation models interventions cumulative regret regression

2022-08-26abs ↗pdf ↗

ExDAG: an MIQP Algorithm for Learning DAGscs.LG

ExDAG solves DAG learning problems with low structural Hamming distance.

problem Learning DAGs with low structural Hamming distance under identifiability assumptions.

method Mixed-integer quadratic programming (MIQP) with branch-and-bound-and-cut algorithm and lazy constraints.

result ExDAG guarantees global convergence and provides a real-time quality assessment.

DAG learning MIQP branch-and-bound-and-cut lazy constraints structural Hamming distance

2024-06-21abs ↗pdf ↗

High-dimensional learning of linear causal networks via inverse covariance estimationstat.ML

We establish a new framework for statistical estimation of directed acyclic graphs (DAGs) when data are generated from a linear, possibly non-Gaussian structural equation model. Our framework consists of two parts: (1) inferring the moralized graph from the support of the inverse covariance matrix; and (2) selecting th…

2013-11-14abs ↗pdf ↗

Pathway Lasso: Estimate and Select Sparse Mediation Pathways with High Dimensional Mediatorsstat.ML

In many scientific studies, it becomes increasingly important to delineate the causal pathways through a large number of mediators, such as genetic and brain mediators. Structural equation modeling (SEM) is a popular technique to estimate the pathway effects, commonly expressed as products of coefficients. However, it …

2016-03-24abs ↗pdf ↗

Scalable Intervention Target Estimation in Linear Modelsstat.ME

Paper proposes scalable algorithm to estimate intervention targets in linear models.

problem Estimating intervention targets in linear models from observational and interventional data.

method The paper proposes a scalable algorithm that estimates intervention sites from the difference between precision matrices of observational and interventional datasets.

result The algorithm consistently identifies all intervention targets and updates observational Markov equivalence classes to interventional ones.

causal inference linear models interventional data precision matrices Markov equivalence

2021-11-15abs ↗pdf ↗

Identifying Patient-Specific Root Causes of Diseasestat.ML

Identifies patient-specific root causes of disease using structural equation models.

problem Detecting significant variables in complex diseases that differ between patients.

method Defining patient-specific root causes as exogenous errors in a structural equation model, quantifying predictivity using Shapley values, and developing a fast algorithm called Root Causal Inference.

result Significant improvements in accuracy by uncovering root causes with large effect sizes at the individual level but clinically insignificant effect sizes at the group level.

structural equation modeling patient-specific root causes Shapley values root cause analysis disease prediction

2022-05-23abs ↗pdf ↗

Applied Causal Inference Powered by ML and AIecon.EM

Book introduces ML and AI for causal inference.

problem Uncertainty in causal relationships.

method Structural equation models, DAGs, SCMs, and Double/Debiased Machine Learning.

result Improved inference in causal models using predictive tools.

causal inference machine learning AI structural equation models structural causal models

2024-03-04abs ↗pdf ↗

Improved Bound for Robust Causal Bandits with Linear Modelsstat.ML

New algorithm reduces regret in CBs with time-varying models.

problem Designing robust interventions in CBs with unknown, fluctuating causal models.

method Proposes a robust CB algorithm with upper and lower bounds on regret.

result Achieves nearly optimal

ilde{\mathcal{O}}(\sqrt{T})

regret under certain conditions.

Causal Bandits Temporal Model Fluctuations Linear Structural Equation Models Cumulative Regret Optimal Regret

2024-05-13abs ↗pdf ↗

Causal Discovery in Linear Models with Unobserved Variables and Measurement Errorcs.LG

Study tackles causal structure learning in linear models with unobserved variables and measurement error.

problem Challenges of unobserved common causes and measurement error in causal structure learning.

method Introduces LV-SEM-ME model with four types of variables and characterizes identifiability under separability condition.

result Establishes form of identification robustness for target effect in broader LV-SEM-ME model.

Causal Discovery Linear Models Unobserved Variables Measurement Error Separability Condition

2024-07-28abs ↗pdf ↗

Semi-Blind Inference of Topologies and Dynamical Processes over Graphscs.LG

Network science provides valuable insights across numerous disciplines including sociology, biology, neuroscience and engineering. A task of major practical importance in these application domains is inferring the network structure from noisy observations at a subset of nodes. Available methods for topology inference t…

2018-05-16abs ↗pdf ↗

Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivitystat.AP

Structural equation models (SEMs) and vector autoregressive models (VARMs) are two broad families of approaches that have been shown useful in effective brain connectivity studies. While VARMs postulate that a given region of interest in the brain is directionally connected to another one by virtue of time-lagged influ…

2016-10-20abs ↗pdf ↗

Learning Causal Graphs in Manufacturing Domains using Structural Equation Modelsstat.ML

New method uses SEMs to uncover cause-effect in manufacturing processes.

problem Complex cause-and-effect relationships in manufacturing processes.

method Using Structural Equation Models with non-linear relationships.

result More informative cause-effect relationships derived from data.

Manufacturing Structural Equation Models Causal Graphs Non-linear Relationships

2022-10-26abs ↗pdf ↗

Parameter-wise co-clustering for high-dimensional datastat.ML

In recent years, data dimensionality has increasingly become a concern, leading to many parameter and dimension reduction techniques being proposed in the literature. A parameter-wise co-clustering model, for data modelled via continuous random variables, is presented. The proposed model, although allowing more flexibi…

2018-08-25abs ↗pdf ↗

Non-explosion of diffusion processes on manifolds with time-dependent metricmath.PR

We study the problem of non-explosion of diffusion processes on a manifold with time-dependent Riemannian metric. In particular we obtain that Brownian motion cannot explode in finite time if the metric evolves under backwards Ricci flow. Our result makes it possible to remove the assumption of non-explosion in the pat…

2009-10-09abs ↗pdf ↗

Nonlinear Causal Discovery via Kernel Anchor Regressionstat.ML

Proposes KAR for nonlinear causal discovery using kernel methods.

problem Learning causal relationships in nonlinear settings.

method Kernel anchor regression (KAR) with improved three-stage nonparametric regression.

result KAR outperforms existing methods in nonlinear causal discovery.

causal discovery nonlinear regression kernel methods structural equation models

2022-10-30abs ↗pdf ↗