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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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8 results for “bow-free”

Beyond Additivity: Causal Discovery in Location-Scale Noise Models with Hidden Variablesstat.ML

Identifying causal direction in location-scale noise models with hidden variables

problem Causal discovery in location-scale noise models with hidden variables

method ADMGs satisfying a bow-free condition

result First identifiability result for causally insufficient models beyond noise additivity

ADMG bow-free identifiability location-scale causal direction

2026-06-06abs ↗pdf ↗

Distributional Equivalence and Structure Learning for Bow-free Acyclic Path Diagramsstat.ML

We consider the problem of structure learning for bow-free acyclic path diagrams (BAPs). BAPs can be viewed as a generalization of linear Gaussian DAG models that allow for certain hidden variables. We present a first method for this problem using a greedy score-based search algorithm. We also prove some necessary and …

2015-08-07abs ↗pdf ↗

Efficiently Deciding Algebraic Equivalence of Bow-Free Acyclic Path Diagramscs.LG

Efficient algorithms decide algebraic constraints of causal graphs.

problem Distinguish causal graphs with latent confounders.

method Study algebraic constraints and propose efficient algorithms.

result Decide equivalence or subset of algebraic constraints.

causal discovery latent confounders algebraic constraints bow-free acyclic path diagrams

2024-06-10abs ↗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 ↗

Robust Identifiability in Linear Structural Equation Models of Causal Inferencecs.LG

We improve robust parameter estimation in causal models from observational data.

problem Robustly estimating parameters in linear structural equation models from observational data.

method Extending Sankararaman et al. (2019) to a broader class of models, providing sufficient conditions for robust identifiability.

result For a large set of parameters, robust identifiability holds and existing algorithms achieve robust identifiability.

robust identifiability linear structural equation models causal inference observational data path models

2020-07-14abs ↗pdf ↗

Learning Linear Non-Gaussian Graphical Models with Multidirected Edgescs.LG

New method learns graph structure with hidden causes from observational data.

problem Learning the structure of linear non-Gaussian models with hidden causes.

method Augments hidden variable structure by learning multidirected edges and uses higher order cumulants.

result Correct structure recovery for bow-free acyclic mixed graphs with multi-directed edges.

Graphical models Hidden causes Non-Gaussian Cumulants Mixed graphs

2020-10-11abs ↗pdf ↗

Differentiable Causal Discovery Under Unmeasured Confoundingcs.LG

New method discovers causal relationships in confounded systems.

problem Discovering causal relationships in systems with unmeasured confounding variables.

method Differentiable algebraic constraints for continuous optimization of ADMGs.

result Effective method for causal discovery in confounded linear systems.

causal discovery confounding unmeasured variables differentiable optimization ADMG

2020-10-14abs ↗pdf ↗

Stability of Linear Structural Equation Models of Causal Inferencecs.LG

We consider the numerical stability of the parameter recovery problem in Linear Structural Equation Model ($\LSEM$) of causal inference. A long line of work starting from Wright (1920) has focused on understanding which sub-classes of $\LSEM$ allow for efficient parameter recovery. Despite decades of study, this questi…

2019-05-16abs ↗pdf ↗