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Aggregation Functions

Causal has three types of Aggregation Functions that determine how a variable 'rolls up' Categories, Time, and Data respectively. You can find this setting by right-clicking on a variable in the spreadsheet.

The default method for all three functions is Sum, but there are other methods available:

  • Average
  • Min / Max (not available for Data / Time Aggregation)
  • Final / Initial (not available for Category Aggregation)
  • Median (not available for Time Aggregation)
  • Formula (not available for Data Aggregation)

Category aggregation

By default, Causal will take the Sum of each category item's value to aggregate the variable. However, there are examples where Sum doesn't make sense, e.g. if you've split Bonus % by Department, it might be more appropriate to see the average of the Departments' Bonus %, not the Sum. See our Category Aggregation page for more info.

Time aggregation

In charts and tables, and in roll-up columns in the spreadsheet, Causal automatically rolls up a variable's values across time. For example: if your model is monthly and you have a Revenue variable, then Causal can automatically summarize the total revenue in each year or each quarter. The Time Aggregation function determines how this roll-up happens, and whilst the default is Sum, there are examples where you'd want other methods (e.g. for Balance Sheet items like Cash, you'd want to take the Final amount for the 2023 balance, not the sum of all the 2023 balances). See our Time Aggregation page for more info.

Note that the Time Aggregation setting of a variable also determines how a variable is 'rolled up' to a model with a higher-level granularity, for example from a Weekly model to a Monthly model. See different time granularities for more info.

Data aggregation

The Data Aggregation function on a variable connected to data determines how Causal aggregates items in a data source, to return the values that you see for the variable. This is usually always going to be Sum, but for example: if you have daily cumulative data in your data source, and are pulling that into a monthly model in Causal, Sum wouldn't make sense (as that would be adding multiple cumulative numbers together), so you might choose Final instead. See our Data Aggregation page for more info.