Causal Embedding

Causal embedding is the process of mapping outcome causes/symptoms to vectors of real numbers.

The illustration below depicts how causal embedding can be used in model training and prediction processing.

Causal Vector

The causal vector contains an element for each observed cause. Each vector element represents a linear equation dimension for the cause in the result/cause model multidimensional space.

Embedding Vectors Creation

Embedding vectors are created by assembling individual elements that represent linear equation dimensions in the result/cause model multidimensional space.

Observed Causes

Observed causes are measured against a specific scale, such as 0-100 or .00-1.00. Examples are medical causes/symptoms such as chest pain and headache.

Predicted Result

A predicted result is the output of the result/cause model prediction processing. An example predicted result in medical diagnosis is influenza.

Result/Cause Associations

Result/cause associations are the pairing of a result with a potential cause or symptom. An example is headache paired with influenza.

Result/Cause Embedding Vectors

Causal embedding vectors are created by assembling individual elements that represent linear equation dimensions in the result/cause model multidimensional space. they are paired with their corresponding result scalar values for use in model training.

Result/Cause Model

A result/cause model is trained and used to predict results. There are a number of model possibilities for use with causal embedding, such as Artificial Neural Networks, Probabilistic Graphical Models, and Support Vector Machines.

Result/Cause Model Training

Result/cause model is trained using causal embedding vectors and result scalar values.

Result/Cause Model Prediction Processing

A causal embedding vector us used as input to prediction processing and a predicted result scalar is the output.