> ## Documentation Index
> Fetch the complete documentation index at: https://docs.summand.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Correlation-association matrix
> Appropriate measure of association for each column pair — Pearson's correlation coefficient, Cramér's V, or correlation ratio, depending on column types.
This component is currently in beta and may be subject to changes or instability.
The **Correlation-Association Matrix** component answers the question: *which columns move together, and how strongly?* It does **not** answer questions of causation — two things happening together does not mean one causes the other.
For every column pair in the dataset, the component selects one of three association measures based on the column types involved, then assembles a summary table sorted by absolute association strength.
## What it computes
The measure used for a given pair depends on the types of the two columns:
| Column pair | Measure | Range |
| ----------------------- | ----------------------------------------------------- | -------- |
| Numeric–numeric | Pearson's correlation coefficient | −1 to +1 |
| Categorical–categorical | Cramér's V statistic (with Bergsma's bias-correction) | 0 to +1 |
| Numeric–categorical | Correlation ratio | 0 to +1 |
Notes:
* Columns that are neither numeric nor categorical (e.g. timestamps, timedelays) are skipped.
* Cramér's V bias correction means a value of 0 can mean either no association or one too weak to distinguish from noise at this sample size.
* Cramér's V and the correlation ratio also report how many categories were involved in each categorical column. Together with the sample size, this helps interpret how meaningful the association value is.
## Inputs
None. The component runs on the full dataset with no configuration required.
## Output shape
```json theme={null}
{
"summary_table": [
{
"column_1": "length_of_stay",
"column_2": "insurance_type",
"association": 0.431,
"method": "correlation_ratio"
},
{
"column_1": "heart_rate",
"column_2": "cholesterol",
"association": 0.287,
"method": "correlation_coefficient"
},
{
"column_1": "blood_type",
"column_2": "icd_code",
"association": 0.104,
"method": "cramer_v_statistic"
}
],
"associations": [
{
"column_pair": "length_of_stay & insurance_type",
"column_1": "length_of_stay",
"column_1_type": "numeric",
"column_2": "insurance_type",
"column_2_type": "categorical",
"sample_size": 4821,
"association_is_computed": true,
"association_type": "correlation_ratio",
"association_value": 0.431,
"number_of_categories": 4
},
{
"column_pair": "blood_type & icd_code",
"column_1": "blood_type",
"column_1_type": "categorical",
"column_2": "icd_code",
"column_2_type": "categorical",
"sample_size": 4821,
"association_is_computed": true,
"association_type": "cramer_v_statistic",
"association_value": 0.104,
"number_of_categories_in_column_1": 4,
"number_of_categories_in_column_2": 812
},
...
]
}
```
`summary_table` contains each pair once, sorted by descending absolute association value. `associations` contains each pair **twice** (both orderings of the columns), so that agent lookups filtering on `column_1` alone can find any pair. The artifact is written as `correlation_association_matrix.json`.
## Display
The component renders a **sortable table** of all column pairs where the association could be computed. Each row shows the two column names, the association value, and the method. The table is sorted by absolute association strength by default.
## Filtering from chat
Summand can query associations in two ways:
```text theme={null}
# Association between two specific columns
analyze({
component: "correlation_association_matrix",
params: { association_between_two_columns: "heart_rate & cholesterol" }
})
# All associations involving one column
analyze({
component: "correlation_association_matrix",
params: { all_associations_with_column: "length_of_stay" }
})
```
The `association_between_two_columns` parameter expects the exact column names joined with `&` as separator — the order does not matter since the `associations` list stores both orderings.
## Use cases
* **Preliminary feature selection** — identify columns that vary strongly together and drop redundant ones before training a model.
* **Identify co-occurring groups** — e.g. patients with diagnoses A and B who are statistically likely to also have diagnosis C.
* **Measure group impact on outcomes** — how does insurance type or care setting relate to length of stay?
* **Exploratory data analysis** — surface unexpected relationships in a new dataset.
## Compute profile
| Profile | Profile |
| ------- | ----------- |
| Fargate | 2 CPU, 8 GB |
The component runs in pandas on the curated Parquet. Pairwise computation scales as O(n\_columns ^ 2); on wide datasets (hundreds of columns) runtime grows accordingly, but the per-pair work is cheap.
## Common gotchas
Two things will cause a column to be excluded: it's neither numeric nor categorical (e.g. a timestamp column) or it's a unique identifier — a column where every value is distinct. Primary-key-style ID columns fall into this last category.
This happens when at least one column in the pair has zero variance (all rows share the same value), when there are no rows where both columns have non-null values simultaneously, when one column's cardinality exactly equals the number of valid rows for that pair (it's trivially, perfectly predictive of the other, so there's nothing informative to measure), or, for a categorical pair, when the contingency table implied by the two columns' cardinalities would be too large to compute safely. The `reason_not_computed` field in the `associations` list explains which case applies.
Correlation, Cramér's V, and the correlation ratio all measure co-movement or predictability, not causation. A high value tells you the columns move together; it says nothing about why.