# Causal Discovery

190 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## Most implemented papers

# DAGs with NO TEARS: Continuous Optimization for Structure Learning

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

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.

# 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

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.

# Learning Sparse Nonparametric DAGs

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