*Since version 7.18*

Impact analysis evaluates the effect of different subsets of evidence on a target variable and/or state. The target is known as the **Hypothesis**.

Impact analysis can be used to:

- See which individual pieces of evidence change the target the most
- See which individual pieces of evidence, when excluded, change the target the most
- See which subsets of evidence which, when included or excluded, change the target the most.

Variable type | Hypothesis | Evidence |
---|---|---|

Discrete | Yes | Yes |

Continuous | Yes | Yes |

Hybrid | Yes | Yes |

We use the term

Noneto refer to the case when none of the evidence (being analyzed) is included.

We use the term

Allto refer to the case when all of the evidence (being analyzed) is included.

The subsets to consider are generated by looking at all possible combinations of evidence, subject to the following constraints.

The number of items of evidence in each subset.

When **Subset method** is set to **Include**, evidence on variables in the subset is included, but not other evidence variables (being analyzed).

When **Subset method** is set to **Exclude**, all evidence is used, except for those in the subset.

For each evidence subset, impact analysis calculates a number of statistics:

The Kullback-Leibler divergence D(P||Q) from the hypothesis query without evidence (Q) to the current combination (P).

This tells us how different V is between the current subset evidence and no evidence.

The Kullback-Leibler divergence D(P||Q) from the hypothesis query with the current subset of evidence (Q) to all evidence (P).

This tells us how different V is between all evidence and the current subset evidence.

When a discrete hypothesis state is included, this reports the probability of that state given the current evidence subset.

When a discrete hypothesis state is included, this reports the difference between the probability of that state given the current evidence subset and no evidence.

When a discrete hypothesis state is included, this reports the difference between the probability of that state given *All* the evidence and the current evidence subset.

When a discrete hypothesis state is included, this reports the ratio between the probability of that state given the current evidence subset and no evidence.

This is also known as the

Normalized Likelihoodin impact analysis.

When a discrete hypothesis state is included, this reports the ratio between the probability of that state given *All* the evidence and the current evidence subset.