This courses introduces causal inference methods, primarily using probabilistic graphical models, to identify and estimate counterfactual quantities as functions of observational data. We will discuss ...

In this paper, researchers conduct a comprehensive review of existing causal reasoning methods for visual representation learning, covering fundamental theories, models, and datasets.

Financial AI is often criticized for being biased—rejecting loans based on gender or credit reputation by ethnicity, or geography. Causal AI eliminates these filtered-out non-causal predictors, ...

Causal models only use past and current neural signals to generate speech, whereas non-causal models use past, present, and future neural signals.

For Causal AI to work in Decision Intelligence, knowledge models are created to help guide the data model by applying constraints and queries. The knowledge model can be built from any number of ...

Zili Dong, Wright’s path analysis - El análisis de caminos de Wright, Theoria: An International Journal for Theory, History and Foundations of Science, Vol. 39, No. 1 (2024), pp. 67-88 ...

World Models: The core. AI requires internal, simulatable representations of its world, which include objects, actors, attributes, spatial/temporal relationships and causal mechanisms.