# Impact analysis

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. ## Support

Variable type Hypothesis Evidence
Discrete Yes Yes
Continuous Yes Yes
Hybrid Yes Yes

We use the term None to refer to the case when none of the evidence (being analyzed) is included.

We use the term All to refer to the case when all of the evidence (being analyzed) is included.

## Subsets

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

### Max evidence subset size

The number of items of evidence in each subset.

### Subset method

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.

## Statistics

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

### Kullback-Leibler divergence (from None)

This is the KL-Divergence D(P||Q) between the target distribution given the current subset evidence P, and the target distribution given None Q.

This tells us how different the target distribution is from the current evidence subset to when no evidence (being analyzed) is set.

### Kullback-Leibler divergence (to All)

This is the KL-Divergence D(P||Q) between the target distribution given All P, and the target distribution given this evidence subset Q.

This tells us how different the target distribution is from when all the evidence (being analyzed) is set vs our evidence subset.

### P(State)

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

### P(StateThisDiffNone)

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 (being analyzed).

### P(StateAllDiffThis)

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.

### P(StateThisLiftNone) / Normalized Likelihood

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 (being analyzed).

This is also known as the Normalized Likelihood in impact analysis.

### P(StateAllLiftThis)

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.