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Causal Inference is a good way to understand the effects of the overlapping
rules of Kant and the behavior of the Schrödinger's cat(=data).

Causal Inference

Kant, The critique of pure reason, Transcenental aesthetic / Schrödinger's cat.

Introduction to Causal Inférences (pdf) is a good way to understand the final behavior of Evenja's paradigm in a “Causal Inference” way.

In this publication, you will find this sentence : Finding answers to questions about the mechanisms by which variables come to take on values, or predicting the value of a variable after some other variable has been manipulated, is characteristic of causal inference. Evenja's paradigm is a proposal of this type of causal inference.

In chapter “2. Manipulating Versus Conditioning”, Evenja has no learning process or statistical probabilities. The behavior of the data is completely and precisely defined by the programmer of the functionality.

More informations about our point of view on chapter “3.1 Causal Bayesian Networks” on the page Evenja versus Bayesian network.

More details about the software implementation on the page Evenja Basics.

fundamentals/causal.txt · Last modified: 2022/02/21 23:34 (external edit)