Temporal Inference
Summary
Temporal Inference using Finite Factored Sets is a novel approach to understanding and analyzing temporal relationships in data. This method, inspired by Pearl’s causal inference paradigm but distinct in its formal approach, replaces directed acyclic graphs with factored sets, which are expressed as Cartesian products. The power of finite factored sets in inferring temporal relations is demonstrated through the introduction of conditional orthogonality, an analog to d-separation in graph-based models. This concept is shown to be equivalent to conditional independence across all probability distributions on a finite factored set, providing a robust framework for temporal analysis. This approach offers a fresh perspective on temporal inference, potentially opening new avenues for research and applications in fields where understanding time-based relationships is crucial.