LLM training data mixture optimization breaks when training pools shift — every prior proxy experiment becomes stale.
Although it is the goal of most statistical investigation, causal inference has traditionally been ignored by statistical theory. Fortunately, there is now intense activity in a number of fields, ...
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 ...
Real-world data (RWD) is increasingly used for causal inference in healthcare research, but generating credible, decision-ready insights requires more than access to data. It demands intentional ...
"I read it as a joke!" one student chortled. "It definitely wasn't completely serious, was it?" another asked as she shook her head in disbelief. The intimate group of nine students—which includes a ...
The surge in enterprise AI has fueled interest in causal analysis. In this piece, I explore the threads that bind cause and effect - and how they can be applied across a range of industry scenarios.
Correspondence to Dr Kaitlin H Wade, Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK; Kaitlin.Wade{at}bristol.ac.uk Kujala provides an insightful review contesting ...
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