mccormick.northwestern.edu

COMP_SCI 374: Causal Graphical Models

We know that correlation does not imply causation, but careful analyses of correlations are often our only way to quantify cause and effect in domains ranging from healthcare to education. This ...
InfoQ

AWS and Microsoft Working Together on PyWhy, the New Home of Causal ML Library DoWhy

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...
Nature

Graphical Models and Bayesian Networks

Graphical models form a cornerstone of modern data analysis by providing a visually intuitive framework to represent and reason about the complex interdependencies among variables. In particular, ...
University of Bristol

Developing methods for complex causal inference

Dr Vanessa Didelez is a statistician developing methods to understand better causal mechanisms, the processes linking cause and effect in complex systems in motion that evolve over time, so-called ...
San Diego Reader

Rethinking Machine Learning: Stuart Frost Makes the Case for Causal AI in Manufacturing

Causal AI transforms key areas of manufacturing by revealing what happens and why. In predictive maintenance, it shifts focus ...
JSTOR Daily

Intervention and Causality: Forecasting Traffic Flows Using a Dynamic Bayesian Network

Real-time traffic flow data across entire networks can be used in a traffic management system to monitor current traffic flows so that traffic can be directed and managed efficiently. Reliable ...
JSTOR Daily

Maximization of Densities of Soil Animals by Foliage Herbivory: Empirical Evidence, Graphical and Conceptual Models

Feeding by consumers on one portion of a plant resource often results in benefits to consumers feeding on other portions of the same resource. Moderate grazing of foliage often increases densities and ...