knowledge-base-3

Causal Discovery

3260 papers • 126 benchmarks • 313 datasets

( Image credit: TCDF )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in causal-discovery-3

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find causal-discovery-3 models and implementations

IntelLabs/causality-lab

3 papers 128

Datasets

BCOPA-CE

CausalBench

Ultra-processed Food Dataset

Subtasks

No subtasks available.

Most implemented papers

DAGs with NO TEARS: Continuous Optimization for Structure Learning

E. Xing, Bryon Aragam, Pradeep Ravikumar, Xun Zheng•Sat Mar 03 2018

This paper forms the structure learning problem as a purely continuous optimization problem over real matrices that avoids this combinatorial constraint entirely and achieves a novel characterization of acyclicity that is not only smooth but also exact.

1191

Content

microsoft/causica

2 papers 391

majianthu/copent

2 papers 38

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Testing conditional independence in supervised learning algorithms

David Watson, Marvin N. Wright•Sun Jan 27 2019

A novel testing procedure that works in conjunction with any valid knockoff sampler, supervised learning algorithm, and loss function is developed, demonstrating convergence criteria for the CPI and developing statistical inference procedures for evaluating its magnitude, significance, and precision.

71 0

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Causal Discovery Toolbox: Uncover causal relationships in Python

Diviyan Kalainathan, Olivier Goudet•Tue Mar 05 2019

The 'cdt' package implements the end-to-end approach, recovering the direct dependencies (the skeleton of the causal graph) and the causal relationships between variables, together with algorithms for pairwise causal discovery such as ANM.

91 0

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Estimating Transfer Entropy via Copula Entropy

Jian Ma•Wed Oct 09 2019

It is proved that TE can be represented with only CE and then a non-parametric method for estimating TE via CE is proposed, which can infer causality relationships from data effectively and hence help to understand the data better.

20 0

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DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization

Bryon Aragam, Pradeep Ravikumar, Kevin Bello•Thu Sep 15 2022

A new acyclicity characterization based on the log-determinant (log-det) function, which leverages the nilpotency property of DAGs and can reach large speed-ups and smaller structural Hamming distances against state-of-the-art methods.

130 0

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Federated Causal Discovery From Interventions

B. Scholkopf, Arash Mehrjou, P. Schwab, Nino Scherrer, Amin Abyaneh, Stefan Bauer•Sun Nov 06 2022

FedCDI is proposed, a federated framework for inferring causal structures from distributed data containing interventional samples that improves privacy by exchanging belief updates rather than raw samples, and introduces a novel intervention-aware method for aggregating individual updates.

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A Survey on Causal Discovery Methods for I.I.D. and Time Series Data

Uzma Hasan, Emam Hossain, Md. Osman Gani•Sun Mar 26 2023

This study presents an extensive discussion on the methods designed to perform causal discovery from both independent and identically distributed (I.I.D.) data and time series data and provides a comprehensive discussion of the algorithms designed to identify causal relations in different settings.

54 0

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Kernel-based Conditional Independence Test and Application in Causal Discovery

B. Scholkopf, J. Peters, Kun Zhang, D. Janzing•Wed Jul 13 2011

A Kernel-based Conditional Independence test (KCI-test) is proposed, by constructing an appropriate test statistic and deriving its asymptotic distribution under the null hypothesis of conditional independence.

687 0

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Discovering Causal Signals in Images

B. Scholkopf, Soumith Chintala, Robert Nishihara, L. Bottou, David Lopez-Paz•Wed May 25 2016

The existence of observable footprints that reveal the causal dispositions of the object categories appearing in collections of images are established and a causal direction classifier is built that achieves state-of-the-art performance on finding the causal direction between pairs of random variables, given samples from their joint distribution.

243 0

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Causal Discovery with Cascade Nonlinear Additive Noise Models

Kun Zhang, Ruichu Cai, Jie Qiao, Zhenjie Zhang, Z. Hao•Tue Apr 30 2019

Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.

27 0

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