Hidden Variable Discovery

Context

• Hidden variables, especially unobserved confounders can be a challenge for estimating effects.
• The Hidden Variable Discovery helps you in the search for such variables.
• It is based on the following principle:
  • Graph theory stipulates that in a serial connection of variables, such as A B C, the variables A and C are marginally dependent.

This report computing the G-test and the independence probability between two variables of the network bounded by a path of length one or two and which is not a V-structure (Analysis menu).

All the existing paths of length one or two (without V-structure) will be tested.

The independence probability will be computed and only the paths which are not independent are kept in the report.

Computing independence between variables:

G-test: The value of the independence test G is computed from the data associated with the network between each pair of variables which are the ends of the paths.

Degree of freedom: Indicates the degree of freedom between the ends of each path.

p-value: Represents the independence probability of the G-test between the ends of each path.