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 ...
Tech Times

LLM Data Mixture Breaks When Training Pools Shift: Causal Inference Offers Fix

LLM training data mixture optimization breaks when training pools shift — every prior proxy experiment becomes stale.
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 ...
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 ...
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 ...
Informationsdienst Wissenschaft

New Department "Computational Precision Nutrition" at DIfE

On July 1, 2026, the new department "Computational Precision Nutrition" commenced its research activities at the German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), led by Prof. Stefan ...
lse

Time Series and Statistical Learning

Time series is data collected over time, and statistical learning is a field of statistics and machine learning that develops algorithms to model and interpret this data. Together, they use ...