Causal Discovery
203 papers with code • 0 benchmarks • 3 datasets
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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 with no code
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.
Bridging Causal Discovery and Large Language Models: A Comprehensive Survey of Integrative Approaches and Future Directions
Causal discovery (CD) and Large Language Models (LLMs) represent two emerging fields of study with significant implications for artificial intelligence.
Metrics on Markov Equivalence Classes for Evaluating Causal Discovery Algorithms
Many state-of-the-art causal discovery methods aim to generate an output graph that encodes the graphical separation and connection statements of the causal graph that underlies the data-generating process.
Discovery of the Hidden World with Large Language Models
The rise of large language models (LLMs) that are trained to learn rich knowledge from the massive observations of the world, provides a new opportunity to assist with discovering high-level hidden variables from the raw observational data.
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.
