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Doubly Robust DDD estimators for the group-time average treatment effects.

ddd.Rd

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 ~ x1 + x2. Default is xformla = ~1 (no covariates).

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 control_group = "notyettreated" which sets as control group the units that have not yet participated in the treatment. The alternative is control_group = "nevertreated" which sets as control group the units that never participate in the treatment and does not change across groups or time periods.

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 "dr" (doubly robust). It computes propensity score using logistic regression and outcome regression using OLS. The alternative are c("reg", "ipw").

weightsname

The name of the column containing the weights. Default is NULL. As part of data processing, weights are enforced to be normalized and have mean 1 across all observations.

boot

Logical. If TRUE, the function computes standard errors using the multiplier bootstrap. Default is FALSE.

nboot

The number of bootstrap samples to be used. Default is NULL. If boot = TRUE, the default is nboot = 999.

cluster

The name of the variable to be used for clustering. The maximum number of cluster variables is 1. Default is NULL. If boot = TRUE, the function computes the bootstrap standard errors clustering at the unit level setting as cluster variable the one in idname.

cband

Logical. If TRUE, the function computes a uniform confidence band that covers all of the average treatment effects with fixed probability 1-alpha. In order to compute uniform confidence bands, boot must also be set to TRUE. The default is FALSE.

alpha

The level of significance for the confidence intervals. Default is 0.05.

use_parallel

Logical. If TRUE, the function runs in parallel processing. Valid only when boot = TRUE. Default is FALSE.

cores

The number of cores to be used in the parallel processing. Default is cores = 1.

inffunc

Logical. If TRUE, the function returns the influence function. Default is FALSE.

skip_data_checks

Logical. If TRUE, the function skips the data checks and go straight to estimation. Default is FALSE.

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)
#> Key: <id>
#>       id state partition  time        y        cov1       cov2      cov3
#>    <int> <num>     <num> <int>    <num>       <num>      <num>     <num>
#> 1:     1     0         0     1 209.9152 -0.97080934 -1.1726958 2.3893945
#> 2:     1     0         0     2 417.5260 -0.97080934 -1.1726958 2.3893945
#> 3:     2     0         0     1 211.4919  0.02591115  0.2763066 0.1063123
#> 4:     2     0         0     2 420.3656  0.02591115  0.2763066 0.1063123
#> 5:     3     0         0     1 221.9431  0.97147321 -0.4292088 0.5012794
#> 6:     3     0         0     2 440.9623  0.97147321 -0.4292088 0.5012794
#>          cov4 cluster
#>         <num>   <int>
#> 1:  0.2174955      39
#> 2:  0.2174955      39
#> 3: -0.1922253      29
#> 4: -0.1922253      29
#> 5:  1.1027248      44
#> 6:  1.1027248      44

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)
#>  Call:
#> ddd(yname = "y", tname = "time", idname = "id", gname = "state", 
#>     pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4, 
#>     data = df, control_group = "nevertreated", est_method = "dr")
#> =========================== DDD Summary ==============================
#>  DR-DDD estimation for the ATT: 
#>      ATT       Std. Error    Pr(>|t|)  [95% Ptwise. Conf. Band]              
#>     -0.0780       0.0828       0.3463      -0.2404       0.0843              
#> 
#>  Note: * indicates that the confidence interval does not contain zero.
#>  --------------------------- Data Info   -----------------------------
#>  Panel data
#>  Outcome variable: y
#>  Qualification variable: partition
#>  No. of units at each subgroup:
#>    treated-and-eligible: 1232
#>    treated-but-ineligible: 1285
#>    eligible-but-untreated: 1256
#>    untreated-and-ineligible: 1227
#>  --------------------------- Algorithms ------------------------------
#>  Outcome Regression estimated using: OLS
#>  Propensity score estimated using: Maximum Likelihood
#>  --------------------------- Std. Errors  ----------------------------
#>  Level of significance:  0.05
#>  Analytical standard errors.
#>  Type of confidence band:  Pointwise Confidence Interval
#>  =====================================================================
#>  See Ortiz-Villavicencio and Sant'Anna (2025) for details.



#----------------------------------------------------------
# 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")
#>  Call:
#> 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")
#> =========================== DDD Summary ==============================
#>  DR-DDD estimation for the ATT(g,t): 
#> Group Time  ATT(g,t)  Std. Error [95% Pointwise  Conf. Band]  
#>   2    2       9.7245     0.3035       9.1297       10.3192  *
#>   2    3      20.0652     0.2920      19.4930       20.6375  *
#>   3    2      -0.0414     0.3073      -0.6437        0.5608   
#>   3    3      25.1409     0.2815      24.5893       25.6925  *
#> 
#>  Note: * indicates that the confidence interval does not contain zero.
#>  --------------------------- Data Info   -----------------------------
#>  Panel data
#>  Outcome variable: y
#>  Qualification variable: partition
#>  Control group: Never Treated
#>  No. of units per treatment group:
#>   Units enabling treatment at period 3: 454
#>   Units enabling treatment at period 2: 351
#>   Units never enabling treatment: 195
#>  --------------------------- Algorithms ------------------------------
#>  Outcome Regression estimated using: OLS
#>  Propensity score estimated using: Maximum Likelihood
#>  --------------------------- Std. Errors  ----------------------------
#>  Level of significance:  0.05
#>  Analytical standard errors.
#>  Type of confidence band:  Pointwise Confidence Interval
#>  =====================================================================
#>  See Ortiz-Villavicencio and Sant'Anna (2025) for details.