Causal Discovery
203 papers with code • 0 benchmarks • 3 datasets
( Image credit: TCDF )
Benchmarks
These leaderboards are used to track progress in Causal Discovery
Libraries
Use these libraries to find Causal Discovery models and implementations
BCOPA-CE
CausalBench
Most implemented papers
DAGs with NO TEARS: Continuous Optimization for Structure Learning
xunzheng/notears
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NeurIPS 2018
This is achieved by a novel characterization of acyclicity that is not only smooth but also exact.
Testing Conditional Independence in Supervised Learning Algorithms
We propose the conditional predictive impact (CPI), a consistent and unbiased estimator of the association between one or several features and a given outcome, conditional on a reduced feature set.
Causal Discovery Toolbox: Uncover causal relationships in Python
FenTechSolutions/CausalDiscoveryToolbox
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This paper presents a new open source Python framework for causal discovery from observational data and domain background knowledge, aimed at causal graph and causal mechanism modeling.
Estimating Transfer Entropy via Copula Entropy
Causal discovery is a fundamental problem in statistics and has wide applications in different fields.
DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization
From the optimization side, we drop the typically used augmented Lagrangian scheme and propose DAGMA ($\textit{DAGs via M-matrices for Acyclicity}$), a method that resembles the central path for barrier methods.
Federated Causal Discovery From Interventions
We propose FedCDI, a federated framework for inferring causal structures from distributed data containing interventional samples.
A Survey on Causal Discovery Methods for I.I.D. and Time Series Data
The ability to understand causality from data is one of the major milestones of human-level intelligence.
Kernel-based Conditional Independence Test and Application in Causal Discovery
Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery.
Discovering Causal Signals in Images
kyrs/NCC-experiments
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CVPR 2017
Our experiments demonstrate the existence of a relation between the direction of causality and the difference between objects and their contexts, and by the same token, the existence of observable signals that reveal the causal dispositions of objects.
Causal Discovery with Cascade Nonlinear Additive Noise Models
In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect.
