Benchmarks
Greatest papers with code
Causal Discovery Toolbox: Uncover causal relationships in Python
• FenTechSolutions/CausalDiscoveryToolbox
• 
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.
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets
We consider causal discovery from time series using conditional independence (CI) based network learning algorithms such as the PC algorithm.
Conditional independence testing based on a nearest-neighbor estimator of conditional mutual information
Combining the local permutation scheme with the kernel tests leads to better calibration, but suffers in power.
Masked Gradient-Based Causal Structure Learning
• xunzheng/notears
• 
We next utilize the augmented form to develop a masked structure learning method that can be efficiently trained using gradient-based optimization methods, by leveraging a smooth characterization on acyclicity and the Gumbel-Softmax approach to approximate the binary adjacency matrix.
Learning Sparse Nonparametric DAGs
• xunzheng/notears
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We develop a framework for learning sparse nonparametric directed acyclic graphs (DAGs) from data.
DAGs with NO TEARS: Continuous Optimization for Structure Learning
NeurIPS 2018
• xunzheng/notears
•
This is achieved by a novel characterization of acyclicity that is not only smooth but also exact.
Causal Discovery with Attention-Based Convolutional Neural Networks
Machine Learning and Knowledge Extraction 2019
• M-Nauta/TCDF
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We therefore present the Temporal Causal Discovery Framework (TCDF), a deep learning framework that learns a causal graph structure by discovering causal relationships in observational time series data.
Causal Discovery with Reinforcement Learning
ICLR 2020
• huawei-noah/trustworthyAI
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The reward incorporates both the predefined score function and two penalty terms for enforcing acyclicity.
Causal Generative Neural Networks
ICLR 2018
• GoudetOlivier/CGNN
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We present Causal Generative Neural Networks (CGNNs) to learn functional causal models from observational data.
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
• loeweX/AmortizedCausalDiscovery
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Standard causal discovery methods must fit a new model whenever they encounter samples from a new underlying causal graph.