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 ...

Causal inference is one of the most important and challenging aims in statistics and data science. Many fields, from clinical medicine to social sciences, strive to use empirical data to understand ...

Over the past several years, the lion’s share of artificial intelligence (AI) investment has poured into training infrastructure—massive clusters designed to crunch through oceans of data, where speed ...

Please join the JHU CFAR Biostatistics and Epidemiology Methodology (BEM) Core on Thursday, September 4, 2025, from 2-3 pm ET for a session covering the fundamentals of causal inference. If you have ...

What is this book about? Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that ...

At the core of causal inference lies the challenge of determining reliable causal graphs solely based on observational data. Since the well-known backdoor criterion depends on the graph, any errors in ...

Abstract: Large-scale multiobjective optimization problems (LSMOPs), characterized by a substantial number of decision variables, pose significant challenges for many existing evolutionary algorithms.

Abstract: Causal inference enables us to move beyond merely observing correlations in understanding the actual causal relationships between variables, but how to connect it with machine learning model ...

Classical machine learning (ML) is remarkably effective at finding patterns and associations in data. It can spot correlations that escape human eyes and minds. Yet the technology suffers from a ...