Causal Discovery
197 papers with code • 0 benchmarks • 3 datasets
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Causal Discovery from Poisson Branching Structural Causal Model Using High-Order Cumulant with Path Analysis
Fortunately, in this work, we found that the causal order from $X$ to its child $Y$ is identifiable if $X$ is a root vertex and has at least two directed paths to $Y$, or the ancestor of $X$ with the most directed path to $X$ has a directed path to $Y$ without passing $X$.
Learning causal graphs using variable grouping according to ancestral relationship
However, when the sample size is small relative to the number of variables, the accuracy of estimating causal graphs using existing methods decreases.
Local Causal Discovery with Linear non-Gaussian Cyclic Models
Local causal discovery is of great practical significance, as there are often situations where the discovery of the global causal structure is unnecessary, and the interest lies solely on a single target variable.
DIGIC: Domain Generalizable Imitation Learning by Causal Discovery
Causality has been combined with machine learning to produce robust representations for domain generalization.
Signature Kernel Conditional Independence Tests in Causal Discovery for Stochastic Processes
Inferring the causal structure underlying stochastic dynamical systems from observational data holds great promise in domains ranging from science and health to finance.
Cause and Effect: Can Large Language Models Truly Understand Causality?
The knowledge from ConceptNet enhances the performance of multiple causal reasoning tasks such as causal discovery, causal identification and counterfactual reasoning.
Impact of Physical Activity on Quality of Life During Pregnancy: A Causal ML Approach
Our result shows that performing adequate physical activity during pregnancy and postpartum improves the QoL by units of 7. 3 and 3. 4 on average in physical health and psychological domains, respectively.
Learning Cyclic Causal Models from Incomplete Data
Under the additive noise model, MissNODAGS learns the causal graph by alternating between imputing the missing data and maximizing the expected log-likelihood of the visible part of the data in each training step, following the principles of the expectation-maximization (EM) framework.
Towards Automated Causal Discovery: a case study on 5G telecommunication data
We introduce the concept of Automated Causal Discovery (AutoCD), defined as any system that aims to fully automate the application of causal discovery and causal reasoning methods.
ChatDiet: Empowering Personalized Nutrition-Oriented Food Recommender Chatbots through an LLM-Augmented Framework
The personal model leverages causal discovery and inference techniques to assess personalized nutritional effects for a specific user, whereas the population model provides generalized information on food nutritional content.