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 ...

The Limits of Causal Inference from Observational Data That Statisticians Hide—And Why We Keep Using It Anyway

Correlation is not causation—despite hearing this warning repeated so often, everyone in the business world says, ...

The Idea That 'Welfare Makes People Stop Working' Was a Statistical Misconception—The Truth Revealed by Causal Inference

People don't work because they have welfare.”Many people insist on this. The employment rate of welfare recipients is indeed ...
umass

Corwin Zigler: Bayesian Causal Inference with Uncertain Physical Process Interference

Abstract: Causal inference with spatial environmental data is often challenging due to the presence of interference: outcomes for observational units depend on some combination of local and non-local ...
BIOENGINEER

Causal AI Predicts IVF Success and Reveals Which Changes Actually Help

For couples undergoing in vitro fertilization, one of the most agonizing questions is also the most basic: what are the real ...
BIOENGINEER

Statisticians Flag Causal Claims Linking Restless Legs Syndrome to Perinatal Depression

A new Letter to the Editor in the Journal of Clinical Sleep Medicine challenges the model specification and causal ...
umass

Do observational causal inference methods really work?

Over the past several decades, multiple statistical methods have been developed to infer the existence and magnitude of causal effects by analyzing observational data. These methods have been widely ...
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 ...