Health researchers need to fully understand the underlying assumptions to uncover cause and effect. Timothy Feeney and Paul Zivich explain Physicians ask, answer, and interpret myriad causal questions ...
The files in this repository comprise the source code for the lecture slides and other content for the course PUBH 7485/8485 Methods for Causal Inference. Although most of statistical inference ...
You train the model once, but you run it every day. Making sure your model has business context and guardrails to guarantee reliability is more valuable than fussing over LLMs. We’re years into the ...
If the hyperscalers are masters of anything, it is driving scale up and driving costs down so that a new type of information technology can be cheap enough so it can be widely deployed. The ...
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 ...
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 ...
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 ...
Despite the contributions of more than 700,000 men to randomized controlled trials (RCTs) of prostate cancer (PC) screening over several decades, it is still unclear whether there is a PC-specific ...
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