Nature

Causal inference and counterfactual prediction in machine learning for actionable healthcare

Advances in computing and machine learning have opened unprecedented paths to processing and inferring knowledge from big data. Deep learning has been game-changing for many analytics challenges, ...
Nature

Causal inference from cross-sectional earth system data with geographical convergent cross mapping

Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models have serious limitations when time series data are ...
eLife

Bayesian causal inference unifies perceptual and neuronal processing of center-surround motion in area MT

This manuscript represents a valuable contribution to understanding motion processing in the visual cortex. Based on a heterogeneous collection of previous empirical findings, the authors show that ...
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 ...
Finextra

Why AI Governance Frameworks Are Failing — and What a Causal Model Does Differently

Every major consultancy is selling an AI governance framework right now. The market is crowded: maturity models, policy templates, ethics checklists, responsible AI principles. Most of them are ...