In the article that accompanies this editorial, Lu et al 5 conducted a systematic review on the use of instrumental variable (IV) methods in oncology comparative effectiveness research. The main ...
The majority of recent empirical papers in operations management (OM) employ observational data to investigate the causal effects of a treatment, such as program or policy adoption. However, as ...
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 ...
Many observational studies aim to make causal inferences about effects of interventions or exposures on health outcomes. This course defines causation, describes how emulating a ‘target trial’ can ...
Most AI systems are trained on historical data. When conditions shift due to changing consumer sentiment, models trained on ...
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 ...
In 2026, neural networks are achieving unprecedented efficiency, multimodal integration, and workflow comprehension, yet benchmarks like MLRegTest reveal persistent struggles with formal rule learning ...
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Why applied statistics is your career superpower
Applied statistics is more than numbers—it’s a way to solve real-world problems in social science, healthcare, business, and beyond. From mastering research design to applying advanced data analysis, ...
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