Just to set expectations, I want to start out by saying that this concept raises a number of red flags for me. I think it might be possible, but I don't know if the performance will scale as you add more columns and more combinations.
You are basically taking one of the worlds fastest aggregation engines, then loading in pre-aggregated data and only using it for filtering. And a good deal of the speed of the tabular engine behind Power BI comes from the fact that it uses a column store, so if you have a visual that only references 4 columns, Power BI only has to scan those 4 columns. But with your non-aggregatable data it will have to scan every "row" of all columns for every visual.
Based on your requirements and the sample data you provided I think the following measure will work
Measure =
// Marital Status Filters
VAR _maritalStatusFilter = FILTER(VALUES(Table1[MaritalStatus]),
IF(ISINSCOPE(Table1[MaritalStatus])
, Table1[MaritalStatus] <> "All Marital Statuses" // if Marital Status is one of the output columns
, Table1[MaritalStatus] = SELECTEDVALUE(Table1[MaritalStatus]) // if Marital Status has a single filter
)
)
VAR _maritalStatusAll = FILTER(ALL(Table1[MaritalStatus])
, IF(NOT(HASONEVALUE(Table1[MaritalStatus])) // if Marital Status has multiple fiters
, Table1[MaritalStatus] = "All Marital Statuses"
,FALSE()
)
)
// Gender Filters
VAR _genderFilter = FILTER(VALUES(Table1[Gender]),
IF(ISINSCOPE(Table1[Gender])
, Table1[Gender] <> "All genders"
, Table1[Gender] = SELECTEDVALUE(Table1[Gender])
)
)
VAR _genderAll = FILTER(ALL(Table1[Gender])
, IF(NOT(HASONEVALUE(Table1[Gender]))
, Table1[Gender] = "All genders"
,FALSE()
)
)
// Year Filter (does not need an "All" filter)
VAR _yearFilter = FILTER(VALUES(Table1[Year])
, Table1[Year] = SELECTEDVALUE(Table1[Year])
)
Var result = CALCULATE(MAX(Table1[m])
, UNION(_genderFilter,_genderAll)
, UNION( _maritalStatusFilter, _maritalStatusAll)
, _yearFilter
)
return result
