Nature

Causal Inference and Bayesian Network Learning

The past decade has witnessed significant advances in causal inference and Bayesian network learning, two intertwined disciplines that allow researchers to discern underlying cause‐and‐effect ...

Researchers Propose a Novel Representation Learning Framework to Address the Lack of Causal Characterization in Deep Learning Models

Recently, researchers introduced a new representation learning framework that integrates causal inference with graph neural networks—CauSkelNet, which can be used to model the causal relationships and ...
JSTOR Daily

FOUNDATIONS OF STRUCTURAL CAUSAL MODELS WITH CYCLES AND LATENT VARIABLES

Structural causal models (SCMs), also known as (nonparametric) structural equation models (SEMs), are widely used for causal modeling purposes. In particular, acyclic SCMs, also known as recursive ...
JSTOR Daily

EXTENDING BAYESIAN STRUCTURAL TIME-SERIES ESTIMATES OF CAUSAL IMPACT TO MANY-HOUSEHOLD CONSERVATION INITIATIVES

The Annals of Applied Statistics, Vol. 12, No. 4 (December 2018), pp. 2517-2539 (23 pages) Government agencies offer economic incentives to citizens for conservation actions, such as rebates for ...
The Atlantic

How a Pioneer of Machine Learning Became One of Its Sharpest Critics

Artificial intelligence owes a lot of its smarts to Judea Pearl. In the 1980s he led efforts that allowed machines to reason probabilistically. Now he’s one of the field’s sharpest critics. In his ...
The Conversation

Coronavirus: country comparisons are pointless unless we account for these biases in testing

Norman Fenton is a Director of Agena Ltd, a company that specialises in risk management for critical systems using Bayesian networks. He also currently receives funding from the EPSRC under project EP ...