ddd {triplediff} | R Documentation |
Doubly Robust DDD estimators for the group-time average treatment effects.
Description
ddd
is the main function for computing the Doubly Robust DDD estimators for the ATT, with balanced panel data.
It can be used with covariates and/or under multiple time periods. At its core, triplediff
employs
the doubly robust estimator for the ATT, which is a combination of the propensity score weighting and the outcome regression.
Furthermore, this package supports the application of machine learning methods for the estimation of the nuisance parameters.
Usage
ddd(
yname,
tname,
idname,
gname,
pname,
xformla,
data,
control_group = NULL,
base_period = NULL,
est_method = "dr",
weightsname = NULL,
boot = FALSE,
nboot = NULL,
cluster = NULL,
cband = FALSE,
alpha = 0.05,
use_parallel = FALSE,
cores = 1,
inffunc = FALSE,
skip_data_checks = FALSE
)
Arguments
yname |
The name of the outcome variable. |
tname |
The name of the column containing the time periods. |
idname |
The name of the column containing the unit id. |
gname |
The name of the column containing the first period when a particular observation is treated. It is a positive number for treated units and defines which group the unit belongs to. It takes value 0 or Inf for untreated units. |
pname |
The name of the column containing the partition variable (e.g., the subgroup identifier). This is an indicator variable that is 1 for the units eligible for treatment and 0 otherwise. |
xformla |
The formula for the covariates to be included in the model. It should be of the form |
data |
A data frame or data table containing the data. |
control_group |
Valid for multiple periods only. The control group to be used in the estimation. Default is |
base_period |
Valid for multiple periods. Choose between a "varying" or "universal" base period. Both yield the same post-treatment ATT(g,t) estimates. Varying base period: Computes pseudo-ATT in pre-treatment periods by comparing outcome changes for a group to its comparison group from t-1 to t, repeatedly changing t. Universal base period: Fixes the base period to (g-1), reporting average changes from t to (g-1) for a group relative to its comparison group, similar to event study regressions. Varying base period reports ATT(g,t) right before treatment. Universal base period normalizes the estimate before treatment to be 0, adding one extra estimate in an earlier period. |
est_method |
The estimation method to be used. Default is |
weightsname |
The name of the column containing the weights. Default is |
boot |
Logical. If |
nboot |
The number of bootstrap samples to be used. Default is |
cluster |
The name of the variable to be used for clustering. The maximum number of cluster variables is 1. Default is |
cband |
Logical. If |
alpha |
The level of significance for the confidence intervals. Default is |
use_parallel |
Logical. If |
cores |
The number of cores to be used in the parallel processing. Default is |
inffunc |
Logical. If |
skip_data_checks |
Logical. If |
Value
A ddd
object with the following basic elements:
ATT |
The average treatment effect on the treated. |
se |
The standard error of the ATT. |
uci |
The upper confidence interval of the ATT. |
lci |
The lower confidence interval of the ATT. |
inf_func |
The estimate of the influence function. |
Examples
#----------------------------------------------------------
# Triple Diff with covariates and 2 time periods
#----------------------------------------------------------
set.seed(1234) # Set seed for reproducibility
# Simulate data for a two-periods DDD setup
df <- gen_dgp_2periods(size = 5000, dgp_type = 1)$data
head(df)
att_22 <- ddd(yname = "y", tname = "time", idname = "id", gname = "state",
pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4,
data = df, control_group = "nevertreated", est_method = "dr")
summary(att_22)
#----------------------------------------------------------
# Triple Diff with multiple time periods
#----------------------------------------------------------
data <- gen_dgp_mult_periods(size = 1000, dgp_type = 1)[["data"]]
ddd(yname = "y", tname = "time", idname = "id",
gname = "state", pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4,
data = data, control_group = "nevertreated", base_period = "varying",
est_method = "dr")