Suppes-Bayesian model

The Suppes-Bayesian model combines ideas from probability and causal reasoning to understand how different factors influence an outcome over time. It uses data to identify which events or conditions are likely causes and how they connect, respecting the natural order—causes happen before effects. By applying principles from the philosopher Suppes, it ensures that identified causes are both probable and temporally prior, helping to construct a logical and data-driven map of causal relationships. This approach is useful in fields like healthcare, where understanding the sequence and likelihood of causes can improve decision-making.