IEEE

Causal Inference in AI Based Decision Support: Beyond Correlation to Causation

Abstract: In the emergent and evolving landscape of artificial intelligence, decision support systems have a vital role in the choice-making functions, right from the health sector to finance. Whereas ...
startupfortune

Aether AI raises $20 million to bet the next AI ceiling is causality, not compute

Aether AI, founded by UCSD professor Biwei Huang, closed a $20 million seed round on June 18, 2026 to build causal world models that understand cause-and-effect relationships rather than statistical ...
IEEE

Federated Experiments: Generative Causal Inference Powered by LLM-Based Agents Simulation and RAG-Based Domain Docking

Abstract: Computational experiments method is an essential tool for analyzing, designing, managing, and integrating complex systems. However, a significant challenge arises in constructing agents with ...
note

The number of folders people have open at the same time varies [A practical example of deep causal distance]

While I was creating new work rules, I took the opportunity to talk to the temporary chat bot about the concept of deep causal distance using different words.Thank you That's perfect!By the way, when ...
GitHub

Tigramite – Causal inference for time series datasets

Update: Tigramite now has a new CausalEffects class that allows to estimate (conditional) causal effects and mediation based on assuming a causal graph. Have a look at the tutorial. Further, Tigramite ...
GitHub

causal-falsify: A Python library with algorithms for falsifying unconfoundedness assumption in a composite dataset from multiple sources.

We have implemented three falsification algorithms, which can be used complementarily: Hierarchical Graphical Independence Constraint (HGIC) Test: This test jointly assesses whether unconfoundedness ...