Collapse effect cells back onto an event-study path

Aggregates a `nabs_effect_cell_tbl` over cohorts to recover a one-dimensional path, returning a `nabs_event_study_tbl` that plugs straight into [nabs_event_plot()]. This makes explicit that the event study is the cohort-collapsed view of the same cells.

Usage

aggregate_effects(cells, by = c("event_time", "calendar_time"))

Arguments

cells

A `nabs_effect_cell_tbl`.

by

Aggregation axis: `"event_time"` (default) or `"calendar_time"`.

Value

A `nabs_event_study_tbl` (with `NA` standard errors).

Details

Point estimates are averaged across cohorts (weighted by `n` when present). Re-aggregated standard errors are **not** computed here – collapsing SEs correctly needs the estimator's replicate draws – so `std.error` and the CI columns are returned as `NA`. Use this for a quick overlay, not for inference.

Examples

raw <- expand.grid(cohort = 3:6, event_time = -2:4)
raw$estimate <- with(raw, ifelse(event_time < 0, 0, 0.2 * event_time))
cells <- as_nabs_effect_cells(raw, method = "FE")
aggregate_effects(cells)
#> ℹ Aggregated over cohorts; std.error is "NA" (re-aggregated SEs need replicate
#>   draws).
#> # <nabs_event_study_tbl>: 7 rows, methods: "FE"
#> # A tibble: 7 × 8
#>    time estimate std.error conf.low conf.high window method outcome
#>   <int>    <dbl>     <dbl>    <dbl>     <dbl> <chr>  <chr>  <chr>  
#> 1    -2      0          NA       NA        NA pre    FE     NA     
#> 2    -1      0          NA       NA        NA pre    FE     NA     
#> 3     0      0          NA       NA        NA post   FE     NA     
#> 4     1      0.2        NA       NA        NA post   FE     NA     
#> 5     2      0.4        NA       NA        NA post   FE     NA     
#> 6     3      0.6        NA       NA        NA post   FE     NA     
#> 7     4      0.8        NA       NA        NA post   FE     NA