Causal Discovery
197 papers with code • 0 benchmarks • 3 datasets
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Recursive Causal Discovery
Presence and identification of removable variables allow recursive approaches for causal discovery, a promising solution that helps to address the aforementioned challenges by reducing the problem size successively.
Detection of Unobserved Common Causes based on NML Code in Discrete, Mixed, and Continuous Variables
Causal discovery in the presence of unobserved common causes from observational data only is a crucial but challenging problem.
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUs
Existing causal discovery methods based on combinatorial optimization or search are slow, prohibiting their application on large-scale datasets.
ROS-Causal: A ROS-based Causal Analysis Framework for Human-Robot Interaction Applications
Deploying robots in human-shared spaces requires understanding interactions among nearby agents and objects.
Optimal Transport for Structure Learning Under Missing Data
Merely filling in missing values with existing imputation methods and subsequently applying structure learning on the complete data is empirical shown to be sub-optimal.
Federated Causal Discovery from Heterogeneous Data
This discrepancy has motivated the development of federated causal discovery (FCD) approaches.
Embracing the black box: Heading towards foundation models for causal discovery from time series data
Our empirical findings suggest that causal discovery in a supervised manner is possible, assuming that the training and test time series samples share most of their dynamics.
Adjustment Identification Distance: A gadjid for Causal Structure Learning
Evaluating graphs learned by causal discovery algorithms is difficult: The number of edges that differ between two graphs does not reflect how the graphs differ with respect to the identifying formulas they suggest for causal effects.
Causal Discovery under Off-Target Interventions
Causal graph discovery is a significant problem with applications across various disciplines.
On the Fly Detection of Root Causes from Observed Data with Application to IT Systems
This paper introduces a new structural causal model tailored for representing threshold-based IT systems and presents a new algorithm designed to rapidly detect root causes of anomalies in such systems.