Causal Discovery

111 papers with code • 0 benchmarks • 3 datasets

( Image credit: TCDF )


Use these libraries to find Causal Discovery models and implementations
2 papers

Most implemented papers

Kernel-based Conditional Independence Test and Application in Causal Discovery

christinaheinze/nonlinearICP-and-CondIndTests 14 Feb 2012

Conditional independence testing is an important problem, especially in Bayesian network learning and causal discovery.

Testing Conditional Independence in Supervised Learning Algorithms

dswatson/cpi 28 Jan 2019

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

majianthu/transferentropy 10 Oct 2019

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 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 6 Mar 2019

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

DMIRLAB-Group/CANM 23 May 2019

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 29 Sep 2019

We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data.

Autoregressive flow-based causal discovery and inference

piomonti/AffineFlowCausalInf 18 Jul 2020

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 4 Nov 2020

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 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.