We are constructing the world's largest causal knowledgebase concerning business and management. Built on knowledge graph technologies and graphic data science, we seek to visually represent and query ...
AI, for example, has made it possible for ... There are two approaches to causal AI that are based on long-known principles: the potential outcomes framework and causal graph models. Both approaches ...
For example, you can use a directed acyclic graph (DAG ... They can help you leverage domain knowledge and expert feedback for causal inference, as they allow you to quantify and test your ...
Specifically, LLMs bring capabilities so far understood to be restricted to humans, such as using collected knowledge to generate causal graphs or identifying background causal context from natural ...
It is the algorithms encoding causal reasoning and domain (e.g., clinical and biological) knowledge that prove transformative. The recent introduction of (health) data science presents an opportunity ...
booktitle={Proceedings of the 27th ACM International Conference on Information and Knowledge ... for learning causal effects from data. We will use the positive review dataset as an example. You will ...
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