Audit the support and assumptions of an intervention design

Reports missing submissions, pre/post support and detection-sensitive outcomes. A clean data audit does not establish causal identification; intervention assignment, contemporaneous changes and spillovers still require substantive evidence.

Usage

lamp_design_audit(
  panel,
  treatment = NULL,
  outcomes = "crime_total",
  min_pre = 6L,
  min_post = 6L
)

Arguments

panel

A lamp_panel.

treatment

Optional dated lamp_treatment() object.

outcomes

Outcome columns under consideration.

min_pre, min_post

Minimum observed months per treated area before and after adoption for the support audit.

Value

A list with issues (check, status, detail), area_support, and summary. Statuses describe data support or required arguments, not certification of causality.

See also

Other diagnostics: lamp_effect_summary(), lamp_elasticity_robustness(), lamp_placebo(), lamp_pretrends(), lamp_trend_sensitivity()

Examples

sim <- lamp_simulate(design = "event", seed = 1)
lamp_design_audit(sim)
#> $issues
#> # A tibble: 5 × 3
#>   check                             status            detail                    
#>   <chr>                             <chr>             <chr>                     
#> 1 observed outcomes and submissions supported         0 of 864 area-months unav…
#> 2 intervention assignment           requires_argument Document assignment, comp…
#> 3 observed stop intensity           supported         0 area-month stop counts …
#> 4 force-month source files          supported         0 crime/search force-mont…
#> 5 detection-sensitive outcomes      concern           Searching can change dete…
#> 
#> $area_support
#> # A tibble: 0 × 0
#> 
#> $summary
#> $summary$n_rows
#> [1] 864
#> 
#> $summary$n_areas
#> [1] 36
#> 
#> $summary$n_months
#> [1] 24
#> 
#> $summary$n_unavailable
#> [1] 0
#> 
#> $summary$source
#> [1] "simulated"
#> 
#> $summary$causal_identification
#> [1] "requires substantive evidence"
#> 
#>