Phys.org

Statistical tools for valid causal inference with fewer assumptions

Causal inference is important in medical research to help determine if treatments are beneficial and if natural exposures are harmful. In many settings, data collection makes causal inference ...
Nature

Causal Inference Using Bayesian Networks

Bayesian networks are probabilistic graphical models that encode conditional dependencies among variables within a directed acyclic graph. In the context of causal inference, these networks provide a ...
BMJ

Causal inference in physical activity epidemiology research: an opportunity for better evidence

Decades of research have established a significant link between physical activity and health, influencing agenda setting, policy making and community awareness.1–4 However, the field continues to ...
JSTOR Daily

Causality In Crisis?: Statistical Methods & Search for Causal Knowledge in Social Sciences

Your institution does not have access to this book on JSTOR. Try searching on JSTOR for other items related to this book. https://doi.org/10.2307/jj.21995551.4 https ...
lse

Causal Inference for Observational and Experimental Studies

This course is available on the MPhil/PhD in Economic Geography, MPhil/PhD in Environmental Economics, MPhil/PhD in International Relations, MPhil/PhD in Regional and Urban Planning Studies, MRes in ...
Finextra

Why Crypto Markets Need Causal AI — And Why Statistical Models Keep Failing Them

The feedback loops that define DeFi, on-chain contagion, and crypto financial crime are not statistical phenomena. They are causal ones. The industry's modelling infrastructure has not caught up. On 9 ...
Frontiers

Volume II: Methods, Tools and Algorithms in Single Cell Bioinformatics

Over the last few years, the single-cell landscape has transitioned from mapping individual cellular transcriptomes to ...