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Child-penalty estimates for many treatment groups and event times

Source: R/multiple_treatment_group_analysis.R
multiple_treatment_group_analysis.Rd

Runs single_treatment_group_analysis over a grid of 2x2 comparisons. For each treatment group \(d\) (age at first birth) and event time \(e = 0, \dots, H\), the target age \(a = d + e\) is compared with the closest not-yet-treated group \(d' = a + 1\) (Leventer 2025, Section 1.3). Pre-treatment event times are estimated against every control group used after treatment, because the identifying assumptions must hold pair by pair (Section 2.3). Results are stacked, and the person-level influence functions that aggregate_estimands and bias_bounds need are attached.

Usage

multiple_treatment_group_analysis(
  data,
  treatment_groups,
  periods_post,
  periods_pre = 4,
  max_control_group = Inf,
  min_pre_age = 0,
  pre = 1,
  Y_name = "Y",
  age_name = "age",
  D_name = "D",
  id_name = "id",
  female_name = "female",
  verbose = TRUE
)

Arguments

data

A data.frame or data.table in long format, one row per person and age; see single_treatment_group_analysis for the requirements. It is not modified.

treatment_groups

Integer vector of ages at first birth \(d\) to estimate (the paper uses 24:34). Groups \(d + 1, \dots, d + H + 1\) must be in data to serve as controls.

periods_post

Whole number \(H \ge 0\): post-treatment event times \(e = 0, \dots, H\) (the paper uses 5).

periods_pre

Whole number \(K \ge 0\) (default 4), or NULL: number of pre-treatment event times for validation tests, \(e = -(pre + K), \dots, -(pre + 1)\) (with the defaults \(-5, \dots, -2\); \(e = -pre\) is the baseline). 0 or NULL skips them.

max_control_group

Largest age at first birth allowed as a control group (the paper's \(D_{max}\); default Inf, no bound). Cells whose control group is above it are not estimated.

min_pre_age

Smallest target age for pre-treatment cells (default 0, no bound). Post-treatment cells are not affected.

pre

Whole number \(\ge 1\) (default 1): the baseline age is \(d - pre\).

Y_name, age_name, D_name, id_name, female_name

Names of the columns holding earnings, age, age at first birth, the person id and the female indicator.

verbose

Logical (default TRUE). Report progress, one line per treatment group.

Value

A data.frame with one block of rows per estimated cell (d, dp, a), sorted by d, event_time and dp; the columns are those of single_treatment_group_analysis. Attributes used by aggregate_estimands and bias_bounds: "if_data" (person-level influence functions per cell), "n_obs" (rows of the prepared data), "n_ids" (persons), "id_info" (id, female, D). Attribute "skipped" lists cells that could not be estimated, with the reason (e.g. an empty gender x group x age cell). Pass the result to the aggregation functions unmodified.

See also

single_treatment_group_analysis, aggregate_estimands, bias_bounds

Examples

# \donttest{
sim <- simulate_data(n_individuals = 2000)
res <- multiple_treatment_group_analysis(sim, treatment_groups = 24:26,
  periods_post = 1, periods_pre = 2, verbose = FALSE)
# The new estimand, by treatment group and event time
subset(res, method == "NTD" & estimand == "rho_effect" & event_time >= 0,
       c(d, event_time, dp, est, se))
#>      d event_time dp        est         se
#> 85  24          0 25 -0.3488283 0.03273725
#> 104 24          1 26 -0.3312687 0.02878966
#> 199 25          0 26 -0.3474838 0.02865365
#> 218 25          1 27 -0.2493495 0.02378134
#> 313 26          0 27 -0.3369988 0.02773765
#> 332 26          1 28 -0.3083804 0.02364997
# Pre-treatment validation tests of NTD, one row per control group
subset(res, method == "Conventional" & event_time < 0,
       c(d, event_time, control_offset, est, se))
#>      d event_time control_offset         est         se
#> 8   24         -3              1 -0.01457375 0.04330571
#> 27  24         -3              2  0.01314518 0.03743401
#> 46  24         -2              1 -0.03754368 0.03958102
#> 65  24         -2              2  0.03862179 0.03559579
#> 122 25         -3              1  0.06846944 0.03595722
#> 141 25         -3              2  0.04753514 0.03251608
#> 160 25         -2              1 -0.00211053 0.03565733
#> 179 25         -2              2  0.01133560 0.03342872
#> 236 26         -3              1 -0.04052352 0.03803836
#> 255 26         -3              2 -0.06175024 0.03378960
#> 274 26         -2              1 -0.05097870 0.03568219
#> 293 26         -2              2 -0.07219692 0.03168115
# }