Causal Discovery
111 papers with code • 0 benchmarks • 3 datasets
( Image credit: TCDF )
Benchmarks
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Libraries
Use these libraries to find Causal Discovery models and implementations
BCOPA-CE
Ultra-processed Food Dataset
Most implemented papers
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.
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.
Estimating Transfer Entropy via Copula Entropy
Causal discovery is a fundamental problem in statistics and has wide applications in different fields.
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.
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.
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.
Learning Sparse Nonparametric DAGs
xunzheng/notears
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We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data.
Autoregressive flow-based causal discovery and inference
piomonti/AffineFlowCausalInf
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We posit that autoregressive flow models are well-suited to performing a range of causal inference tasks - ranging from causal discovery to making interventional and counterfactual predictions.
Causal Autoregressive Flows
piomonti/carefl
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We exploit the fact that autoregressive flow architectures define an ordering over variables, analogous to a causal ordering, to show that they are well-suited to performing a range of causal inference tasks, ranging from causal discovery to making interventional and counterfactual predictions.
DiBS: Differentiable Bayesian Structure Learning
larslorch/dibs
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NeurIPS 2021
In this work, we propose a general, fully differentiable framework for Bayesian structure learning (DiBS) that operates in the continuous space of a latent probabilistic graph representation.
