Causal Discovery
204 papers with code • 0 benchmarks • 3 datasets
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Benchmarks
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Libraries
Use these libraries to find Causal Discovery models and implementations
BCOPA-CE
CausalBench
Latest papers with no code
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
In particular, we are interested in discovering instance-level causal structures in an unsupervised manner.
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating
However, this assumption complicates the application of such models to real data, given that the causal relationships between features are unknown in most cases.
Variational DAG Estimation via State Augmentation With Stochastic Permutations
Estimating the structure of a Bayesian network, in the form of a directed acyclic graph (DAG), from observational data is a statistically and computationally hard problem with essential applications in areas such as causal discovery.
Multi-modal Causal Structure Learning and Root Cause Analysis
Effective root cause analysis (RCA) is vital for swiftly restoring services, minimizing losses, and ensuring the smooth operation and management of complex systems.
Causal Discovery by Kernel Deviance Measures with Heterogeneous Transforms
To go about capturing these discrepancies between cause and effect remains to be a challenge and many current state-of-the-art algorithms propose to compare the norms of the kernel mean embeddings of the conditional distributions.
Comparative Study of Causal Discovery Methods for Cyclic Models with Hidden Confounders
In the last 50 years, many causal discovery algorithms have emerged, but most of them are applicable only under the assumption that the systems have no feedback loops and that they are causally sufficient, i. e. that there are no unmeasured subsystems that can affect multiple measured variables.
Causal Layering via Conditional Entropy
Under appropriate assumptions and conditioning, we can separate the sources or sinks from the remainder of the nodes by comparing their conditional entropy to the unconditional entropy of their noise.
Functional Linear Non-Gaussian Acyclic Model for Causal Discovery
To expand this concept, we extend the notion of variables to encompass vectors and even functions, leading to the Functional Linear Non-Gaussian Acyclic Model (Func-LiNGAM).
Use of Prior Knowledge to Discover Causal Additive Models with Unobserved Variables and its Application to Time Series Data
Moreover, by incorporating the prior knowledge that causes precedes their effects in time, we extend the first algorithm to the second method for causal discovery in time series data.
Causal Discovery in Financial Markets: A Framework for Nonstationary Time-Series Data
A deeper comprehension of financial markets necessitates understanding not only the statistical dependencies among various entities but also the causal dependencies.
