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README.md
In triplediff: Triple-Difference Estimators
Triple Differences Estimators 
triplediff is an R package for computing average treatment effects
in Triple Differences Designs (also known as
Difference-in-Differences-in-Differences or DDD). DDD designs are widely
used in empirical work to relax parallel trends assumptions in
Difference-in-Differences settings. This package provides functions to
estimate group-time average treatment effect and event-study type
estimands associated with DDD designs. The setups allowed are:
- Two-periods or Multiple Periods DDD.
- Single Treatment Date and Variation in Treatment Timing (i.e.,
staggered adoption).
- Conditional and Unconditional DDD parallel trends assumptions.
The triplediff package implements the framework proposed in:
Installation
You can install the latest development version of triplediff from
Github with:
# install.packages("devtools")
devtools::install_github("marcelortizv/triplediff")
library(triplediff)
Quick Start: How to use triplediff
We provide a quick start example to illustrate the main features of the
package. The UX is designed to be similar to the did package, so users
familiar with it should feel comfortable using triplediff.
ddd is the main function for computing the DDD estimators proposed in
Ortiz-Villavicencio and Sant’Anna
(2025). At its core, ddd employs
regression adjustment, inverse probability weighing, and doubly robust
estimators that are valid under conditional DDD parallel trends.
To use ddd, the minimal data requirements include:
-
id: Unit unique identifier (e.g., firm, individual, etc.). Its
column name is associated with parameter idname.
-
period: Time identifier (e.g., year, month, etc.). Its column name
is associated with parameter tname.
-
outcome: Outcome variable of interest. Its column name is associated
with parameter yname.
-
state: First period when treatment is enabled for a particular unit.
It is a positive number for treated units and defines which group the
unit belongs to. It takes value 0 or Inf for never-enabling units.
Its column name is associated with parameter gname.
-
partition: This is an indicator variable that is 1 for the units
eligible for treatment and 0 otherwise. Its column name is associated
with parameter pname.
The following are two simplified examples of how to use the ddd
function. The first one is for a two-period DDD setup, while the second
one is for a multiple-period DDD setup with staggered treatment
adoption.
Case: Two-Periods DDD with covariates
First, we simulate some data with a built-in function gen_dgp_2periods
that generates a two-period DDD setup with a single treatment date. This
function receives the number of units and the type of DGP design to
generate. The gen_dgp_2periods function returns a data frame with the
required columns for the ddd function with 4 covariates.
set.seed(1234) # Set seed for reproducibility
# Simulate data for a two-periods DDD setup
df <- gen_dgp_2periods(
size = 5000, # Number of units
dgp_type = 1 # Type of DGP design
)$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
Now we can estimate the average treatment effect on the treated (ATT)
using the ddd function.
# Estimate the average treatment effect on the treated, ATT(2,2)
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.
Case: Multiple Periods DDD with staggered treatment adoption
Next, we can simulate some data with a built-in function
gen_dgp_mult_periods that generates a multiple-period DDD setup with
staggered treatment adoption. This function receives the number of units
and the type of DGP design to generate. The gen_dgp_mult_periods
function returns a data frame with the required columns for the ddd
function with 4 covariates.
set.seed(1234) # Set seed for reproducibility
# Simulate data for a multiple-period DDD setup with staggered treatment adoption
data <- gen_dgp_mult_periods(size = 500, dgp_type = 1)$data
head(data)
#> Key: <id>
#> id state partition time y cov1 cov2 cov3
#> <int> <num> <num> <int> <num> <num> <num> <num>
#> 1: 1 3 1 1 1371.1923 -0.97080934 1.3995704 1.48834130
#> 2: 1 3 1 2 1690.9157 -0.97080934 1.3995704 1.48834130
#> 3: 1 3 1 3 2034.5661 -0.97080934 1.3995704 1.48834130
#> 4: 2 2 1 1 934.0812 0.02591115 -0.9747527 0.01714001
#> 5: 2 2 1 2 1189.5495 0.02591115 -0.9747527 0.01714001
#> 6: 2 2 1 3 1444.1072 0.02591115 -0.9747527 0.01714001
#> cov4 cluster
#> <num> <int>
#> 1: 0.3853346 27
#> 2: 0.3853346 27
#> 3: 0.3853346 27
#> 4: -0.7822000 45
#> 5: -0.7822000 45
#> 6: -0.7822000 45
Now we can estimate the group-time average treatment effect on the
treated using the ddd function.
# Estimate the group-time average treatment effect in DDD
att_gt <- ddd(yname = "y", tname = "time", idname = "id", gname = "state",
pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4,
data = data, control_group = "nevertreated",
base_period = "universal", est_method = "dr")
summary(att_gt)
#> Call:
#> ddd(yname = "y", tname = "time", idname = "id", gname = "state",
#> pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4,
#> data = data, control_group = "nevertreated", base_period = "universal",
#> 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 1 0.0000 NA NA NA
#> 2 2 10.4901 0.3613 9.7819 11.1983 *
#> 2 3 20.5986 0.3868 19.8405 21.3568 *
#> 3 1 -0.4593 0.3660 -1.1767 0.2580
#> 3 2 0.0000 NA NA NA
#> 3 3 24.5983 0.3333 23.9450 25.2515 *
#>
#> 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: 198
#> Units enabling treatment at period 2: 195
#> Units never enabling treatment: 107
#> --------------------------- 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.
Then, we can aggregate the group-time average treatment effect on the
treated to obtain the event-study type estimates. The agg_ddd function
allows us to aggregate the results from the ddd function.
es_e <- agg_ddd(att_gt, type = "eventstudy")
summary(es_e)
#> Call:
#> agg_ddd(ddd_obj = att_gt, type = "eventstudy")
#> ========================= DDD Aggregation ============================
#> Overall summary of ATT's based on event-study/dynamic aggregation:
#> ATT Std. Error [ 95% Conf. Int.]
#> 19.0983 0.3213 18.4685 19.7281 *
#>
#> Event Study:
#> Event time Estimate Std. Error [95% Ptwise. Conf. Band]
#> -2 -0.4593 0.3660 -1.1767 0.2580
#> -1 0.0000 NA NA NA
#> 0 17.5980 0.4095 16.7955 18.4005 *
#> 1 20.5986 0.3868 19.8405 21.3568 *
#>
#> Note: * indicates that the confidence interval does not contain zero.
#> --------------------------- Data Info -----------------------------
#> Outcome variable: y
#> Qualification variable: partition
#> Control group: Never Treated
#> --------------------------- Std. Errors ----------------------------
#> Level of significance: 0.05
#> Analytical standard errors.
#> =====================================================================
#> See Ortiz-Villavicencio and Sant'Anna (2025) for details.
From these event-study type estimates, we can plot the results using the
ggplot library to create crisp visualizations.
es_df <- with(es_e$aggte_ddd, {
tibble::tibble(period = egt,
estimate = att.egt,
ci_lower = estimate - crit.val.egt * se.egt,
ci_upper = estimate + crit.val.egt * se.egt)
})
all_per <- sort(unique(es_df$period))
p <- ggplot(es_df, aes(period, estimate)) +
geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper),
width = .15,
position = position_dodge(width = .3)) +
geom_point(size = 2.2,
position = position_dodge(width = .3)) +
geom_hline(yintercept = 0, linewidth = .3) +
geom_vline(xintercept = -1, linetype = "dashed",
linewidth = .3) +
scale_x_continuous(
breaks = all_per
) +
labs(title = "",
x = "Event Time",
y = "Treatment Effect") +
theme_bw(base_size = 12) +
theme(plot.title = element_text(hjust = .25),
legend.position = "right")
p
Finally, we can also estimate the group-time average treatment effect
using a GMM-based estimator with not-yet-treated units as comparison
group. This is done by setting the control_group parameter to
"notyettreated" in the ddd function.
# do the same but using GMM-based notyettreated
att_gt_nyt <- ddd(yname = "y", tname = "time", idname = "id", gname = "state",
pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4,
data = data, control_group = "notyettreated",
base_period = "universal", est_method = "dr")
summary(att_gt_nyt)
#> Call:
#> ddd(yname = "y", tname = "time", idname = "id", gname = "state",
#> pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4,
#> data = data, control_group = "notyettreated", base_period = "universal",
#> 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 1 0.0000 NA NA NA
#> 2 2 10.1894 0.2759 9.6486 10.7302 *
#> 2 3 20.5986 0.3868 19.8405 21.3568 *
#> 3 1 -0.4593 0.3660 -1.1767 0.2580
#> 3 2 0.0000 NA NA NA
#> 3 3 24.5983 0.3333 23.9450 25.2515 *
#>
#> Note: * indicates that the confidence interval does not contain zero.
#> --------------------------- Data Info -----------------------------
#> Panel data
#> Outcome variable: y
#> Qualification variable: partition
#> Control group: Not yet Treated (GMM-based)
#> No. of units per treatment group:
#> Units enabling treatment at period 3: 198
#> Units enabling treatment at period 2: 195
#> Units never enabling treatment: 107
#> --------------------------- 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.
From this result, we can observe the standard errors for $ATT(2,2)$ are
lower than the ones obtained with the control_group = "nevertreated"
option since we leverage the additional information from the
not-yet-treated units in the estimation.
- ⚠️ We recommend users to read our paper for further details on the
GMM-based procedure and its difference with other estimators relying
in not-yet-treated units as comparison group.
Release Note 📢
The current version is released in an alpha stage: the core features
are implemented and made available so users can try them out and provide
feedback. Please, be aware that it remains under active development, so
long-term stability is not guaranteed—APIs may evolve, adding more
features and/or breaking changes can occur at any time, without a
deprecation cycle.
Use GitHub Issues
to report bugs, request features or provide feedback.
🚀 Currently Supported
- ️✅ Two-period DDD with single treatment date.
- ️✅ Multiple-period DDD with single treatment date.
- ️✅ DDD with staggered treatment adoption (i.e., more than two periods
and variation in treatment timing).
- ️✅ Aggregations procedures (e.g., event-study type estimates).
- ️✅ GMM-based estimations with not-yet-treated units as comparison
group.
⚠️ Not Yet Supported
- 🔲 Built-in plotting capabilities for visualizing results.
- This can be done easily by users. E.g., event-study type estimates
can be plotted using
ggplot2. See the quick start example.
- 🔲 User-specified data-driven models for estimations of nuisance
parameters.
- 🔲 Repeated cross-sectional data.
- 🔲 Unbalanced panel data.
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triplediff documentation built on Aug. 8, 2025, 7:33 p.m.
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Triple Differences Estimators
triplediff is an R package for computing average treatment effects in Triple Differences Designs (also known as Difference-in-Differences-in-Differences or DDD). DDD designs are widely used in empirical work to relax parallel trends assumptions in Difference-in-Differences settings. This package provides functions to estimate group-time average treatment effect and event-study type estimands associated with DDD designs. The setups allowed are:
- Two-periods or Multiple Periods DDD.
- Single Treatment Date and Variation in Treatment Timing (i.e., staggered adoption).
- Conditional and Unconditional DDD parallel trends assumptions.
The triplediff package implements the framework proposed in:
Installation
You can install the latest development version of triplediff from
Github with:
# install.packages("devtools")
devtools::install_github("marcelortizv/triplediff")
library(triplediff)
Quick Start: How to use triplediff
We provide a quick start example to illustrate the main features of the
package. The UX is designed to be similar to the did package, so users
familiar with it should feel comfortable using triplediff.
ddd is the main function for computing the DDD estimators proposed in
Ortiz-Villavicencio and Sant’Anna
(2025). At its core, ddd employs
regression adjustment, inverse probability weighing, and doubly robust
estimators that are valid under conditional DDD parallel trends.
To use ddd, the minimal data requirements include:
-
id: Unit unique identifier (e.g., firm, individual, etc.). Its column name is associated with parameteridname. -
period: Time identifier (e.g., year, month, etc.). Its column name is associated with parametertname. -
outcome: Outcome variable of interest. Its column name is associated with parameteryname. -
state: First period when treatment is enabled for a particular unit. It is a positive number for treated units and defines which group the unit belongs to. It takes value0orInffor never-enabling units. Its column name is associated with parametergname. -
partition: This is an indicator variable that is 1 for the units eligible for treatment and 0 otherwise. Its column name is associated with parameterpname.
The following are two simplified examples of how to use the ddd
function. The first one is for a two-period DDD setup, while the second
one is for a multiple-period DDD setup with staggered treatment
adoption.
Case: Two-Periods DDD with covariates
First, we simulate some data with a built-in function gen_dgp_2periods
that generates a two-period DDD setup with a single treatment date. This
function receives the number of units and the type of DGP design to
generate. The gen_dgp_2periods function returns a data frame with the
required columns for the ddd function with 4 covariates.
set.seed(1234) # Set seed for reproducibility
# Simulate data for a two-periods DDD setup
df <- gen_dgp_2periods(
size = 5000, # Number of units
dgp_type = 1 # Type of DGP design
)$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
Now we can estimate the average treatment effect on the treated (ATT)
using the ddd function.
# Estimate the average treatment effect on the treated, ATT(2,2)
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.
Case: Multiple Periods DDD with staggered treatment adoption
Next, we can simulate some data with a built-in function
gen_dgp_mult_periods that generates a multiple-period DDD setup with
staggered treatment adoption. This function receives the number of units
and the type of DGP design to generate. The gen_dgp_mult_periods
function returns a data frame with the required columns for the ddd
function with 4 covariates.
set.seed(1234) # Set seed for reproducibility
# Simulate data for a multiple-period DDD setup with staggered treatment adoption
data <- gen_dgp_mult_periods(size = 500, dgp_type = 1)$data
head(data)
#> Key: <id>
#> id state partition time y cov1 cov2 cov3
#> <int> <num> <num> <int> <num> <num> <num> <num>
#> 1: 1 3 1 1 1371.1923 -0.97080934 1.3995704 1.48834130
#> 2: 1 3 1 2 1690.9157 -0.97080934 1.3995704 1.48834130
#> 3: 1 3 1 3 2034.5661 -0.97080934 1.3995704 1.48834130
#> 4: 2 2 1 1 934.0812 0.02591115 -0.9747527 0.01714001
#> 5: 2 2 1 2 1189.5495 0.02591115 -0.9747527 0.01714001
#> 6: 2 2 1 3 1444.1072 0.02591115 -0.9747527 0.01714001
#> cov4 cluster
#> <num> <int>
#> 1: 0.3853346 27
#> 2: 0.3853346 27
#> 3: 0.3853346 27
#> 4: -0.7822000 45
#> 5: -0.7822000 45
#> 6: -0.7822000 45
Now we can estimate the group-time average treatment effect on the
treated using the ddd function.
# Estimate the group-time average treatment effect in DDD
att_gt <- ddd(yname = "y", tname = "time", idname = "id", gname = "state",
pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4,
data = data, control_group = "nevertreated",
base_period = "universal", est_method = "dr")
summary(att_gt)
#> Call:
#> ddd(yname = "y", tname = "time", idname = "id", gname = "state",
#> pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4,
#> data = data, control_group = "nevertreated", base_period = "universal",
#> 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 1 0.0000 NA NA NA
#> 2 2 10.4901 0.3613 9.7819 11.1983 *
#> 2 3 20.5986 0.3868 19.8405 21.3568 *
#> 3 1 -0.4593 0.3660 -1.1767 0.2580
#> 3 2 0.0000 NA NA NA
#> 3 3 24.5983 0.3333 23.9450 25.2515 *
#>
#> 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: 198
#> Units enabling treatment at period 2: 195
#> Units never enabling treatment: 107
#> --------------------------- 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.
Then, we can aggregate the group-time average treatment effect on the
treated to obtain the event-study type estimates. The agg_ddd function
allows us to aggregate the results from the ddd function.
es_e <- agg_ddd(att_gt, type = "eventstudy")
summary(es_e)
#> Call:
#> agg_ddd(ddd_obj = att_gt, type = "eventstudy")
#> ========================= DDD Aggregation ============================
#> Overall summary of ATT's based on event-study/dynamic aggregation:
#> ATT Std. Error [ 95% Conf. Int.]
#> 19.0983 0.3213 18.4685 19.7281 *
#>
#> Event Study:
#> Event time Estimate Std. Error [95% Ptwise. Conf. Band]
#> -2 -0.4593 0.3660 -1.1767 0.2580
#> -1 0.0000 NA NA NA
#> 0 17.5980 0.4095 16.7955 18.4005 *
#> 1 20.5986 0.3868 19.8405 21.3568 *
#>
#> Note: * indicates that the confidence interval does not contain zero.
#> --------------------------- Data Info -----------------------------
#> Outcome variable: y
#> Qualification variable: partition
#> Control group: Never Treated
#> --------------------------- Std. Errors ----------------------------
#> Level of significance: 0.05
#> Analytical standard errors.
#> =====================================================================
#> See Ortiz-Villavicencio and Sant'Anna (2025) for details.
From these event-study type estimates, we can plot the results using the
ggplot library to create crisp visualizations.
es_df <- with(es_e$aggte_ddd, {
tibble::tibble(period = egt,
estimate = att.egt,
ci_lower = estimate - crit.val.egt * se.egt,
ci_upper = estimate + crit.val.egt * se.egt)
})
all_per <- sort(unique(es_df$period))
p <- ggplot(es_df, aes(period, estimate)) +
geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper),
width = .15,
position = position_dodge(width = .3)) +
geom_point(size = 2.2,
position = position_dodge(width = .3)) +
geom_hline(yintercept = 0, linewidth = .3) +
geom_vline(xintercept = -1, linetype = "dashed",
linewidth = .3) +
scale_x_continuous(
breaks = all_per
) +
labs(title = "",
x = "Event Time",
y = "Treatment Effect") +
theme_bw(base_size = 12) +
theme(plot.title = element_text(hjust = .25),
legend.position = "right")
p
Finally, we can also estimate the group-time average treatment effect
using a GMM-based estimator with not-yet-treated units as comparison
group. This is done by setting the control_group parameter to
"notyettreated" in the ddd function.
# do the same but using GMM-based notyettreated
att_gt_nyt <- ddd(yname = "y", tname = "time", idname = "id", gname = "state",
pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4,
data = data, control_group = "notyettreated",
base_period = "universal", est_method = "dr")
summary(att_gt_nyt)
#> Call:
#> ddd(yname = "y", tname = "time", idname = "id", gname = "state",
#> pname = "partition", xformla = ~cov1 + cov2 + cov3 + cov4,
#> data = data, control_group = "notyettreated", base_period = "universal",
#> 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 1 0.0000 NA NA NA
#> 2 2 10.1894 0.2759 9.6486 10.7302 *
#> 2 3 20.5986 0.3868 19.8405 21.3568 *
#> 3 1 -0.4593 0.3660 -1.1767 0.2580
#> 3 2 0.0000 NA NA NA
#> 3 3 24.5983 0.3333 23.9450 25.2515 *
#>
#> Note: * indicates that the confidence interval does not contain zero.
#> --------------------------- Data Info -----------------------------
#> Panel data
#> Outcome variable: y
#> Qualification variable: partition
#> Control group: Not yet Treated (GMM-based)
#> No. of units per treatment group:
#> Units enabling treatment at period 3: 198
#> Units enabling treatment at period 2: 195
#> Units never enabling treatment: 107
#> --------------------------- 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.
From this result, we can observe the standard errors for $ATT(2,2)$ are
lower than the ones obtained with the control_group = "nevertreated"
option since we leverage the additional information from the
not-yet-treated units in the estimation.
- ⚠️ We recommend users to read our paper for further details on the GMM-based procedure and its difference with other estimators relying in not-yet-treated units as comparison group.
Release Note 📢
The current version is released in an alpha stage: the core features are implemented and made available so users can try them out and provide feedback. Please, be aware that it remains under active development, so long-term stability is not guaranteed—APIs may evolve, adding more features and/or breaking changes can occur at any time, without a deprecation cycle.
Use GitHub Issues to report bugs, request features or provide feedback.
🚀 Currently Supported
- ️✅ Two-period DDD with single treatment date.
- ️✅ Multiple-period DDD with single treatment date.
- ️✅ DDD with staggered treatment adoption (i.e., more than two periods and variation in treatment timing).
- ️✅ Aggregations procedures (e.g., event-study type estimates).
- ️✅ GMM-based estimations with not-yet-treated units as comparison group.
⚠️ Not Yet Supported
- 🔲 Built-in plotting capabilities for visualizing results.
- This can be done easily by users. E.g., event-study type estimates
can be plotted using
ggplot2. See the quick start example. - 🔲 User-specified data-driven models for estimations of nuisance parameters.
- 🔲 Repeated cross-sectional data.
- 🔲 Unbalanced panel data.
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