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.

168,657 papers · 148 categories

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48 results for “linear structural causal models”

Causal Discovery in Linear Structural Causal Models with Deterministic Relationscs.LG

Extends linear structural causal models to include deterministic relations and latent confounders for causal discovery.

problem Causal discovery in linear SCMs with deterministic relations and latent confounders.

method Extended existing results to include deterministic relations and latent confounders, derived necessary and sufficient conditions for unique identifiability, proposed an algorithm for recovery.

result First work on identifiability results for causal discovery under latent confounding and deterministic relationships.

causal discovery linear structural causal models deterministic relations latent confounders identifiability

2021-10-30abs ↗pdf ↗

Structure Learning for Cyclic Linear Causal Modelsmath.ST

We solve structure learning for cyclic linear causal models using observational data.

problem Learning the structure of cyclic linear causal models from observational data.

method Assuming simple graphs, we use a criterion for distributional equivalence and implement a greedy search method.

result We show that simple cyclic models are of expected dimension and justify score-based methods for structure learning.

Cyclic Graphs Linear Causal Models Observational Data Structure Learning Score-Based Methods

2020-06-10abs ↗pdf ↗

Identification in Tree-shaped Linear Structural Causal Modelscs.AI

New method identifies causal parameters in tree-shaped linear models using cycles.

problem Identifying causal parameters from correlations in tree-shaped linear models.

method Investigates tree-shaped linear models, uses missing cycles to identify causal parameters, solves quadratic equations.

result Shows how missing cycles can be combined to obtain a unique solution for causal parameters.

tree-shaped models linear causal models missing cycles quadratic equations causal parameter identification

2022-03-03abs ↗pdf ↗

Bayesian learning of Causal Structure and Mechanisms with GFlowNets and Variational Bayescs.LG

Paper introduces VBG for Bayesian causal structure and mechanism learning.

problem Bayesian causal structure learning with uncertainty over models.

method Variational Bayes-DAG-GFlowNet (VBG) method.

result VBG outperforms existing methods in modeling posterior over DAGs and mechanisms.

Bayesian causal structure learning Variational Bayes GFlowNets Uncertainty over models Acyclic graphs

2022-11-04abs ↗pdf ↗

Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confoundersstat.ML

We address the problem of causal discovery from data, making use of the recently proposed causal modeling framework of modular structural causal models (mSCM) to handle cycles, latent confounders and non-linearities. We introduce σ-connection graphs (σ-CG), a new class of mixed graphs (containing undirected, bidirected…

2018-07-09abs ↗pdf ↗

Induced Covariance for Causal Discovery in Linear Sparse Structurescs.LG

New method discovers causal relationships in sparse linear data.

problem Discovering cause-effect relationships in sparse linear data.

method Uses structural matrix to reconstruct data and identify causal structures without independence tests.

result Outperforms existing methods in sparse causal structure recovery.

causal discovery linear sparse structures structural matrix sparse data directed acyclic graphs (DAGs)

2024-10-02abs ↗pdf ↗

Causal Discovery for Linear Non-Gaussian Models with Disjoint Cyclesmath.ST

New algorithm for learning causal structures with disjoint cycles in linear non-Gaussian models.

problem Learning causal structures with cycles in linear non-Gaussian models.

method Characterizing when graphs determine the same model, using quadratic and cubic polynomial relations, and a strategy of decorrelating cycles and multivariate regression.

result Consistent and computationally efficient algorithm for learning causal structures with disjoint cycles.

Causal discovery Linear non-Gaussian models Cycles Disjoint cycles Polynomial relations

2025-07-14abs ↗pdf ↗

Trek-Based Parameter Identification for Linear Causal Models With Arbitrarily Structured Latent Variablesmath.ST

Develops a method to identify causal effects in linear models with latent variables.

problem Identifying causal effects in models with latent variables that are not independent.

method A novel graphical criterion and an integer linear program algorithm.

result Sufficient condition for identifying causal effects by rational formulas in the covariance matrix.

causal inference linear models latent variables integer programming graphical criterion

2025-07-24abs ↗pdf ↗

Embracing Discrete Search: A Reasonable Approach to Causal Structure Learningstat.ML

FLOP algorithm speeds up causal structure learning for linear models.

problem Efficiently learning causal structures from discrete data.

method FLOP algorithm combines fast parent selection and iterative score updates.

result FLOP finds highly accurate causal structures with near-perfect recovery.

causal discovery linear models discrete search score-based iterative search

2025-10-06abs ↗pdf ↗

Causal Discovery for Linear DAGs with Dependent Latent Variables via Higher-order Cumulantscs.LG

Paper proposes a new method to identify causal graphs with latent variables using higher-order cumulants.

problem Estimating causal directed acyclic graphs with latent confounders.

method Uses higher-order cumulants to identify causal structures among observed and latent variables.

result Validates the proposed algorithm through simulations and real-world data.

Causal Discovery Linear DAGs Dependent Latent Variables Higher-order Cumulants LvLiNGAM

2025-10-16abs ↗pdf ↗

Latent Variable Models for Bayesian Causal Discoverycs.LG

A model learns causal representations from high-dimensional data.

problem Challenges in learning causal representations from high-dimensional data.

method Formulated a latent variable decoder model, Decoder BCD, for Bayesian causal discovery.

result Shows that using known intervention targets as labels helps in unsupervised Bayesian inference over structure and parameters.

Bayesian causal discovery latent variable models causal representation high dimensional data linear Gaussian additive noise

2022-07-12abs ↗pdf ↗

Inference of Causal Effects when Control Variables are Unknownstat.ME

New method infers causal effects without knowing control variables.

problem Inference errors when control variables are unknown.

method Proposes a method for inferring causal effects when control variables are unknown.

result Proves method yields asymptotically valid confidence intervals for average causal effects.

causal inference unknown control variables asymptotically valid linear structural causal models

2020-12-15abs ↗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 ↗

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 ↗

Estimating Causal Effects in Gaussian Linear SCMs with Finite Datacs.LG

Estimates causal effects in Gaussian Linear SCMs with finite data.

problem Estimating causal effects from observational data with latent confounders.

method Centralized Gaussian Linear SCMs (CGL-SCMs) and EM-based estimation algorithm.

result Learned CGL-SCM parameters accurately recover causal distributions from finite observational samples.

Causal inference Gaussian Linear SCMs Latent confounders EM algorithm Finite data

2026-01-08abs ↗pdf ↗

Rank-Based Causal Discovery for Post-Nonlinear Modelsstat.ML

New method for learning causal relationships in PNL models.

problem Learning causal relationships from empirical observations in PNL models.

method Rank-based methods to estimate non-linear functions, disentangling from independence tests.

result Consistent method for PNL causal discovery, validated in experiments.

causal discovery post-nonlinear models rank-based methods non-linear functions independence tests

2023-02-23abs ↗pdf ↗

Efficient Symbolic Computations for Identifying Causal Effectsstat.ML

Efficient algorithm for identifying causal effects in linear models.

problem Determining causal effects from observational data under latent confounding.

method Symbolic computation and efficient algorithm for finding identifying formulas.

result Proves the existence of identifying formulas of a specified degree in quasi-polynomial time.

causal inference symbolic computation identifiability latent confounding linear models

2026-04-22abs ↗pdf ↗

Causal Discovery in Linear Latent Variable Models Subject to Measurement Errorcs.LG

We tackle causal discovery in linear systems with measurement error and unobserved causes.

problem Causal discovery in linear systems with measurement error and unobserved causes.

method Characterization of identifiability based on the mixing matrix, proposing causal structure learning methods.

result The structure of causal models can be identified under certain faithfulness assumptions.

Causal Discovery Linear Systems Measurement Error Unobserved Causes Faithfulness Assumption

2022-11-08abs ↗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 ↗

Multi-Domain Empirical Bayes for Linearly-Mixed Causal Representationsstat.ML

Empirical Bayes improves causal representation learning across multiple domains.

problem Estimating causal representations from data across multiple domains.

method Developed an EB

f

-modeling algorithm for linearly-mixed causal representations.

result Our method achieves more accurate estimation of causal variables than other methods.

Empirical Bayes Causal Representation Learning Multi-Domain Linear Measurement Model Score Matching

2026-03-19abs ↗pdf ↗

Bayesian Model Averaging for Causality Estimation and its Approximation based on Gaussian Scale Mixture Distributionsstat.ME

Bayesian model averaging improves causal effect estimation by averaging over multiple models.

problem Estimating causal effects under linear Structural Causal Models (SCMs).

method Bayesian model averaging using Gaussian scale mixture distributions for computational efficiency.

result Bayesian model averaging is optimal for causal effect estimation.

Bayesian model averaging causal effect estimation Bayesian inference Gaussian scale mixture Bayesian model selection

2021-03-15abs ↗pdf ↗

Bayesian estimation of possible causal direction in the presence of latent confounders using a linear non-Gaussian acyclic structural equation model with individual-specific effectsstat.ML

We consider learning the possible causal direction of two observed variables in the presence of latent confounding variables. Several existing methods have been shown to consistently estimate causal direction assuming linear or some type of nonlinear relationship and no latent confounders. However, the estimation resul…

2013-10-24abs ↗pdf ↗

Regularizing towards Causal Invariance: Linear Models with Proxiescs.LG

Proposes a method to create robust linear models with noisy proxies of unobserved variables.

problem Learning robust linear models to handle interventions on unobserved variables with noisy proxies.

method Regularization term that balances in-distribution performance and robustness to interventions.

result Single proxy can create prediction optimal estimators under interventions of bounded strength.

causal inference linear models robustness proxy variables interventions

2021-03-03abs ↗pdf ↗

Learning Structural Causal Models from Ordering: Identifiable Flow Modelscs.LG

Flow models recover causal transformations from observational data and a valid ordering.

problem Causal inference with only observational data and a valid causal ordering.

method Flow models that can recover component-wise, invertible transformations of exogenous variables.

result Flow models outperform previous methods and deliver consistent performance across various structural causal models.

causal inference flow models observational data valid ordering invertible transformations

2024-12-13abs ↗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 ↗

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 ↗

Estimation of Structural Causal Model via Sparsely Mixing Independent Component Analysisstat.ML

New method estimates causal structure from sparse data.

problem Inferring causal structure from sparse observational data.

method Log-likelihood of sparsely mixed ICA with penalty terms.

result Proposed method outperforms existing methods.

causal inference sparse structure sparsely mixed ICA log-likelihood natural gradient

2020-09-07abs ↗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 ↗

Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysismath.DS

The paper introduces Causal Neural Operators to approximate operators in stochastic analysis.

problem Leveraging temporal structure in non-linear operators for deep learning models.

method Designing a deep learning model framework for infinite-dimensional linear metric spaces.

result Causal Neural Operators can uniformly approximate Hölder or smooth trace class operators.

Causal Neural Operators Stochastic Analysis Deep Learning Banach Spaces Temporal Structure

2022-10-24abs ↗pdf ↗

Measurement Dependence Inducing Latent Causal Modelsstat.ML

We consider the task of causal structure learning over measurement dependence inducing latent (MeDIL) causal models. We show that this task can be framed in terms of the graph theoretic problem of finding edge clique covers,resulting in an algorithm for returning minimal MeDIL causal models (minMCMs). This algorithm is…

2019-10-19abs ↗pdf ↗

Discovering Mixtures of Structural Causal Models from Time Series Datacs.LG

New method discovers causal models from mixed time series data.

problem Discovering causal relationships from heterogeneous time series data.

method Variational inference-based framework MCD for linear and nonlinear causal models.

result Method outperforms state-of-the-art benchmarks in causal discovery tasks.

causal discovery time series data mixture models variational inference linear models

2023-10-10abs ↗pdf ↗

Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Modelscs.LG

New method detects causal relationships from noisy measurements.

problem Discover causal relationships from noisy, imperfect measurements.

method Transformed Independent Noise (TIN) condition and ordered group decomposition.

result Identifies causal graph structure without over-complete ICA.

causal discovery measurement error linear non-Gaussian independence testing TIN condition

2022-10-20abs ↗pdf ↗

Effective Bayesian Causal Inference via Structural Marginalisation and Autoregressive Orderscs.LG

Bayesian causal inference method improves accuracy over traditional approaches.

problem Bayesian marginalisation over causal models is computationally infeasible.

method Decomposes structure marginalisation into causal orders and DAGs, using Gaussian processes for mechanisms and ARCO for orders.

result Method outperforms state-of-the-art in structure learning and inference.

Bayesian causal inference structure learning autoregressive orders Gaussian processes interventional distributions

2024-02-22abs ↗pdf ↗

Causal Discovery of Linear Non-Gaussian Causal Models with Unobserved Confoundingstat.ML

New algorithm identifies causal effects in latent confounding models.

problem Identifying causal effects in linear non-Gaussian models with latent confounding.

method Recursive algorithm using rank conditions on higher-order cumulants.

result Algorithm achieves comparable performance to overcomplete ICA without knowing the number of latent variables.

causal discovery linear models non-Gaussian latent confounding rank conditions

2024-08-09abs ↗pdf ↗

Score-based Causal Representation Learning with Interventionsstat.ML

Paper recovers latent causal structure and linear transformation from indirect observations.

problem Recovering latent causal structure and linear transformation from indirect observations.

method Established sufficient conditions for DAG recovery, leveraged score function properties, and used soft/hard interventions.

result Perfect recovery of latent DAG structure and linear transformation up to scaling using soft interventions, hard interventions with additional hypothesis testing.

causal representation learning latent transformation interventional score DAG recovery two-layer neural networks

2023-01-19abs ↗pdf ↗

Near-Optimal Experiment Design in Linear non-Gaussian Cyclic Modelsstat.ML

Optimizes experiment design for causal structure learning in linear models with cycles.

problem Causal structure learning from combined observational and interventional data in linear non-Gaussian cyclic models.

method Combinatorial characterization of equivalence classes, adaptive stochastic optimization, greedy policy with near-optimal performance guarantee, sampling-based estimator for reward function.

result Optimal experiment design reduces the equivalence class of causal graphs to a single true graph with a small number of interventions.

causal inference experiment design linear models non-Gaussian cyclic models

2025-09-25abs ↗pdf ↗

Directed Cyclic Graph for Causal Discovery from Multivariate Functional Datastat.ME

We introduce a model for causal structure learning from multivariate functional data, even when graphs have cycles.

problem Discovering causal relationships from multivariate functional data with cycles.

method Functional linear structural equation model with a low-dimensional causal embedded space.

result The proposed model is causally identifiable under standard assumptions.

causal discovery functional data multivariate structural equation model Bayesian inference

2023-10-31abs ↗pdf ↗

A direct method for estimating a causal ordering in a linear non-Gaussian acyclic modelcs.LG

Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the datagenerating process of variables. Recently, it was shown that use of non-Gaussianity identifies a causal ordering …

2014-08-09abs ↗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 ↗

DeepCausalMMM: A Deep Learning Framework for Marketing Mix Modeling with Causal Structure Learningcs.LG

DeepCausalMMM models marketing impacts using deep learning and causal inference.

problem Traditional MMM approaches struggle with non-linear dynamics and temporal patterns.

method Combines deep learning, causal inference, and marketing science. Uses GRUs for temporal patterns and DAG structure for channel dependencies.

result Captures non-linear dynamics and temporal patterns in marketing impacts.

Marketing Mix Modeling Deep Learning Causal Inference Temporal Patterns Non-Linear Dynamics

2025-10-15abs ↗pdf ↗

Discovering Hierarchical Latent Capabilities of Language Models via Causal Representation Learningcs.LG

The study uncovers latent capabilities of language models via causal representation learning.

problem Rigorous causal evaluations of language model capabilities are challenging due to confounding effects and computational costs.

method Proposes a causal representation learning framework to identify latent capability factors as causally interrelated after controlling for a common confounder (base model).

result Identifies a three-node linear causal structure explaining performance variations across 1500 models and six benchmarks.

language models causal representation learning latent capabilities benchmark performance confounding effects

2025-06-12abs ↗pdf ↗

Socioeconomic disparities and COVID-19: the causal connectionscs.LG

This study uses causal Shapley values to analyze how socioeconomic factors cause the spread of COVID-19.

problem Understanding how socioeconomic factors cause the spread of COVID-19.

method The study employs an explanatory framework from cooperative game theory augmented with do calculus, specifically causal Shapley values, to analyze the causal connections.

result The causal Shapley values reveal distinct advantages of non-linear machine learning models over linear models in multivariate analysis.

causal Shapley values cooperative game theory do calculus socioeconomic disparities COVID-19

2022-01-18abs ↗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 ↗

A PC Algorithm for Max-Linear Bayesian Networksstat.ML

New PCstar algorithm discovers causal structure of max-linear Bayesian networks.

problem Discovering causal structure in max-linear Bayesian networks due to non-faithfulness.

method PC algorithm modified with

C^\ast

-separation assumptions.

result PCstar algorithm can orient additional edges not possible with standard PC algorithm.

max-linear Bayesian networks PC algorithm Causal discovery faithfulness conditional independence

2025-08-19abs ↗pdf ↗

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglementstat.ML

New algorithm disentangles latent features without strict assumptions.

problem Disentangling complex data-generating mechanisms into causally interpretable latent features.

method Linear CRL algorithm with topological ordering, pruning, and disentanglement.

result Recovering latent causal features up to an equivalence class under weaker assumptions.

Causal inference Artificial intelligence Latent features Topological ordering Disentanglement

2025-09-26abs ↗pdf ↗

Causal discovery under mean independence and linearitystat.ME

New method recovers causal order from dependent data.

problem Causal discovery methods fail with shared volatility or common scale effects.

method Linear Mean-Independent Acyclic Model (LiMIAM) with mean-independence restrictions.

result Compatible causal order can be recovered from dependent disturbances.

causal discovery mean independence linear model LiMIAM LiNGAM

2026-05-06abs ↗pdf ↗

DirectLiNGAM: A direct method for learning a linear non-Gaussian structural equation modelstat.ML

Structural equation models and Bayesian networks have been widely used to analyze causal relations between continuous variables. In such frameworks, linear acyclic models are typically used to model the data-generating process of variables. Recently, it was shown that use of non-Gaussianity identifies the full structur…

2011-01-13abs ↗pdf ↗

Regret Analysis of Bandit Problems with Causal Background Knowledgestat.ML

We study how to learn optimal interventions sequentially given causal information represented as a causal graph along with associated conditional distributions. Causal modeling is useful in real world problems like online advertisement where complex causal mechanisms underlie the relationship between interventions and …

2019-10-11abs ↗pdf ↗