Causal Discovery
203 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
BCOPA-CE
CausalBench
Latest papers
Shapley-PC: Constraint-based Causal Structure Learning with Shapley Values
Causal Structure Learning (CSL), amounting to extracting causal relations among the variables in a dataset, is widely perceived as an important step towards robust and transparent models.
Bayesian causal discovery from unknown general interventions
We consider the problem of learning causal Directed Acyclic Graphs (DAGs) using combinations of observational and interventional experimental data.
Causal Structure Learning Supervised by Large Language Model
Causal discovery from observational data is pivotal for deciphering complex relationships.
Causal Interpretation of Self-Attention in Pre-Trained Transformers
The structural equation model can be interpreted, in turn, as a causal structure over the input symbols under the specific context of the input sequence.
Meek Separators and Their Applications in Targeted Causal Discovery
In our work, we focus on two such well-motivated problems: subset search and causal matching.
Robustness of Algorithms for Causal Structure Learning to Hyperparameter Choice
We find that, while the choice of algorithm remains crucial to obtaining state-of-the-art performance, hyperparameter selection in ensemble settings strongly influences the choice of algorithm, in that a poor choice of hyperparameters can lead to analysts using algorithms which do not give state-of-the-art performance for their data.
Fast Scalable and Accurate Discovery of DAGs Using the Best Order Score Search and Grow-Shrink Trees
However, the accuracy and execution time of learning algorithms generally struggle to scale to problems with hundreds of highly connected variables -- for instance, recovering brain networks from fMRI data.
Identifying and Adapting Transformer-Components Responsible for Gender Bias in an English Language Model
iabhijith/bias-causal-analysis
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Language models (LMs) exhibit and amplify many types of undesirable biases learned from the training data, including gender bias.
Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs
In the context of inferring a Bayesian network structure (directed acyclic graph, DAG for short), we devise a non-reversible continuous time Markov chain, the "Causal Zig-Zag sampler", that targets a probability distribution over classes of observationally equivalent (Markov equivalent) DAGs.
CausalTime: Realistically Generated Time-series for Benchmarking of Causal Discovery
jarrycyx/unn
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This study introduces the CausalTime pipeline to generate time-series that highly resemble the real data and with ground truth causal graphs for quantitative performance evaluation.
