README.md
In bcallaway11/pte: Panel Treatment Effects
Panel Treatment Effects (pte) Package 
The pte package compartmentalizes the steps needed to implement
estimators of group-time average treatment effects (and their
aggregations) in order to make it easier to apply the same sorts of
arguments outside of their “birthplace” in the literature on
difference-in-differences.
This code is lightweight, only works for balanced panels, and has
minimal error checking. That said, it should be useful projects that
build on top of group-time average treatment effects in order to deliver
estimates of causal effects in panel data settings.
The main function is called pte. The most important paramters that it
takes in are subset_fun and attgt_fun. These are functions that the
user should pass to pte.
subset_fun takes in the overall data, a group, a time period, and
possibly other arguments and returns a data.frame containing the
relevant subset of the data, an outcome, and whether or not a unit
should be considered to be in the treated or comparison group for that
group/time. There is one example of a relevant subset function provided
in the package: the two_by_two_subset
function.
This function takes an original dataset, subsets it into pre- and
post-treatment periods and denotes treated and untreated units. This
particular subset is perhaps the most common/important one for thinking
about treatment effects with panel data.
The other main function is attgt_fun. This function should be able to
take in the correct subset of data, possibly along with other arguments
to the function, and report an ATT for that subset. With minor
modification, this function should be availble for most any sort of
treatment effects application — for example, if you can solve the
baseline 2x2 case in difference in differences, you should use that
function here, and the pte package will take care of dealing with the
variation in treatment timing.
If attgt_fun returns an influence function, then the pte package
will also conduct inference using the multiplier bootstrap (which is
fast) and produce uniform confidence bands (which adjust for multiple
testing).
The default output of pte is an overall treatment effect on the
treated (i.e., across all groups that participate in the treatment in
any time period) and dynamic effects (i.e., event studies). More
aggregations are possible, but these seem to be the leading cases;
aggregations of group-time average treatment effects are discussed at
length in Callaway and Sant’Anna
(2021).
Here are a few examples:
Example 1: Difference in differences
The did package, which is based
on Callaway and Sant’Anna
(2021), includes
estimates of group-time average treatment effects, ATT(g,t), based on
a difference in differences identification strategy. The following
example demonstrates that it is easy to compute group-time average
treatment effects using difference in differences using the pte
package. [Note: This is definitely not the recommended way of doing
this as there is very little error handling, etc. here, but it is rather
a proof of concept. You should use the did package for this case.]
This example reproduces DID estimates of the effect of the minimum wage
on employment using data from the did package.
library(did)
data(mpdta)
did_res <- pte(yname="lemp",
gname="first.treat",
tname="year",
idname="countyreal",
data=mpdta,
setup_pte_fun=setup_pte,
subset_fun=two_by_two_subset,
attgt_fun=did_attgt,
xformla=~lpop)
summary(did_res)
#>
#> Overall ATT:
#> ATT Std. Error [ 95% Conf. Int.]
#> -0.0323 0.0126 -0.0569 -0.0076 *
#>
#>
#> Dynamic Effects:
#> Event Time Estimate Std. Error [95% Conf. Band]
#> -3 0.0269 0.0168 -0.0159 0.0697
#> -2 -0.0050 0.0125 -0.0368 0.0269
#> -1 -0.0229 0.0126 -0.0550 0.0093
#> 0 -0.0201 0.0131 -0.0534 0.0132
#> 1 -0.0547 0.0179 -0.1003 -0.0091 *
#> 2 -0.1382 0.0409 -0.2422 -0.0341 *
#> 3 -0.1069 0.0341 -0.1936 -0.0202 *
#> ---
#> Signif. codes: `*' confidence band does not cover 0
ggpte(did_res)
What’s most interesting here, is that the only “new” code that needs to
be writte is in the did_attgt
function.
You will see that this is a very small amount of code.
Example 2: Policy Evaluation during a Pandemic
As a next example, consider trying to estimate effects of Covid-19
related policies during a pandemic. The estimates below are for the
effects of state-leve shelter-in-place orders during the early part of
the pandemic.
Callaway and Li (2021) argue that a
particular unconfoundedness-type strategy is more appropriate in this
context than DID-type strategies due to the spread of Covid-19 cases
being highly nonlinear. However, they still deal with the challenge of
variation in treatment timing. Therefore, it is still useful to think
about group-time average treatment effects, but the DID strategy should
be replaced with their particular unconfoundedness type assumption.
The pte package is particularly useful here.
# formula for covariates
xformla <- ~ current + I(current^2) + region + totalTestResults
covid_res <- pte(yname="positive",
gname="group",
tname="time.period",
idname="state_id",
data=covid_data2,
setup_pte_fun=setup_pte_basic,
subset_fun=two_by_two_subset,
attgt_fun=covid_attgt,
xformla=xformla,
max_e=21,
min_e=-10)
#> Warning in compute.aggte(MP = MP, type = type, balance_e = balance_e, min_e =
#> min_e, : Simultaneous conf. band is somehow smaller than pointwise one using
#> normal approximation. Since this is unusual, we are reporting pointwise
#> confidence intervals
summary(covid_res)
#>
#> Overall ATT:
#> ATT Std. Error [ 95% Conf. Int.]
#> 14.8882 58.1647 -99.1125 128.8888
#>
#>
#> Dynamic Effects:
#> Event Time Estimate Std. Error [95% Conf. Band]
#> -10 -3.7266 3.3841 -13.4426 5.9893
#> -9 2.6607 1.5017 -1.6509 6.9722
#> -8 0.8290 2.1465 -5.3339 6.9919
#> -7 5.2843 1.9978 -0.4516 11.0203
#> -6 2.8555 1.8211 -2.3730 8.0841
#> -5 1.3589 4.0879 -10.3778 13.0956
#> -4 0.3294 3.9431 -10.9916 11.6504
#> -3 -4.2227 6.9336 -24.1295 15.6841
#> -2 -3.8447 2.4340 -10.8329 3.1434
#> -1 -0.2234 3.3015 -9.7021 9.2553
#> 0 -10.8156 8.8849 -36.3249 14.6936
#> 1 -13.7998 12.2977 -49.1073 21.5077
#> 2 -7.8432 11.9865 -42.2575 26.5711
#> 3 -4.5541 11.7315 -38.2362 29.1279
#> 4 -3.5368 12.7993 -40.2847 33.2110
#> 5 8.5221 11.1569 -23.5102 40.5544
#> 6 1.1140 17.8634 -50.1732 52.4012
#> 7 6.6384 24.7105 -64.3072 77.5841
#> 8 7.1288 23.8552 -61.3613 75.6189
#> 9 10.8758 30.7177 -77.3170 99.0687
#> 10 17.5057 34.2477 -80.8221 115.8335
#> 11 40.8318 46.6352 -93.0614 174.7250
#> 12 48.6134 57.4288 -116.2692 213.4960
#> 13 52.4228 64.1772 -131.8350 236.6806
#> 14 50.2000 47.2508 -85.4608 185.8608
#> 15 68.2960 66.0155 -121.2395 257.8315
#> 16 44.7305 85.7855 -201.5661 291.0272
#> 17 61.4670 76.4826 -158.1205 281.0544
#> 18 50.4635 114.1011 -277.1294 378.0564
#> 19 47.3392 129.9716 -325.8193 420.4976
#> 20 28.6326 121.7077 -320.7994 378.0646
#> 21 4.3445 133.7291 -379.6020 388.2910
#> ---
#> Signif. codes: `*' confidence band does not cover 0
ggpte(covid_res) + ylim(c(-1000,1000))
What’s most interesting is just how little code needs to be written
here. The only new code required is the ppe::covid_attgt function
which is available
here,
and, as you can see, this is very simple.
Example 3: Empirical Bootstrap
The code above used the multiplier bootstrap. The great thing about the
multiplier bootstrap is that it’s fast. But in order to use it, you have
to work out the influence function for the estimator of ATT(g,t).
Although I pretty much always end up doing this, it can be tedious, and
it can be nice to get a working version of the code for a project going
before working out the details on the influence function.
The pte package can be used with the empirical bootstrap. There are a
few limitations. First, it’s going to be substantially slower. Second,
this code just reports pointwise confidence intervals. However, this
basically is set up to fit into my typical workflow, and I see this as a
way to get preliminary results.
Let’s demonstrate it. To do this, consider the same setup as in Example
1, but where no influence function is returned. Let’s write the code for
this:
# did with no influence function
did_attgt_noif <- function(gt_data, xformla, ...) {
# call original function
did_gt <- did_attgt(gt_data, xformla, ...)
# remove influence function
did_gt$inf_func <- NULL
did_gt
}
Now, we can show the same sorts of results as above
did_res_noif <- pte(yname="lemp",
gname="first.treat",
tname="year",
idname="countyreal",
data=mpdta,
setup_pte_fun=setup_pte,
subset_fun=two_by_two_subset,
attgt_fun=did_attgt_noif, #this is only diff.
xformla=~lpop)
summary(did_res_noif)
#>
#> Overall ATT:
#> ATT Std. Error [ 95% Conf. Int.]
#> -0.0323 0.0107 -0.0533 -0.0113 *
#>
#>
#> Dynamic Effects:
#> Event Time Estimate Std. Error [95% Pointwise Conf. Band]
#> -3 0.0269 0.0151 -0.0026 0.0565
#> -2 -0.0050 0.0115 -0.0275 0.0176
#> -1 -0.0229 0.0153 -0.0529 0.0072
#> 0 -0.0201 0.0102 -0.0400 -0.0002 *
#> 1 -0.0547 0.0172 -0.0885 -0.0210 *
#> 2 -0.1382 0.0391 -0.2148 -0.0616 *
#> 3 -0.1069 0.0379 -0.1813 -0.0325 *
#> ---
#> Signif. codes: `*' confidence band does not cover 0
ggpte(did_res_noif)
What’s exciting about this is just how little new code needs to be
written.
bcallaway11/pte documentation built on Oct. 20, 2023, 3:21 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Panel Treatment Effects (pte) Package
The pte package compartmentalizes the steps needed to implement
estimators of group-time average treatment effects (and their
aggregations) in order to make it easier to apply the same sorts of
arguments outside of their “birthplace” in the literature on
difference-in-differences.
This code is lightweight, only works for balanced panels, and has minimal error checking. That said, it should be useful projects that build on top of group-time average treatment effects in order to deliver estimates of causal effects in panel data settings.
The main function is called pte. The most important paramters that it
takes in are subset_fun and attgt_fun. These are functions that the
user should pass to pte.
subset_fun takes in the overall data, a group, a time period, and
possibly other arguments and returns a data.frame containing the
relevant subset of the data, an outcome, and whether or not a unit
should be considered to be in the treated or comparison group for that
group/time. There is one example of a relevant subset function provided
in the package: the two_by_two_subset
function.
This function takes an original dataset, subsets it into pre- and
post-treatment periods and denotes treated and untreated units. This
particular subset is perhaps the most common/important one for thinking
about treatment effects with panel data.
The other main function is attgt_fun. This function should be able to
take in the correct subset of data, possibly along with other arguments
to the function, and report an ATT for that subset. With minor
modification, this function should be availble for most any sort of
treatment effects application — for example, if you can solve the
baseline 2x2 case in difference in differences, you should use that
function here, and the pte package will take care of dealing with the
variation in treatment timing.
If attgt_fun returns an influence function, then the pte package
will also conduct inference using the multiplier bootstrap (which is
fast) and produce uniform confidence bands (which adjust for multiple
testing).
The default output of pte is an overall treatment effect on the
treated (i.e., across all groups that participate in the treatment in
any time period) and dynamic effects (i.e., event studies). More
aggregations are possible, but these seem to be the leading cases;
aggregations of group-time average treatment effects are discussed at
length in Callaway and Sant’Anna
(2021).
Here are a few examples:
Example 1: Difference in differences
The did package, which is based
on Callaway and Sant’Anna
(2021), includes
estimates of group-time average treatment effects, ATT(g,t), based on
a difference in differences identification strategy. The following
example demonstrates that it is easy to compute group-time average
treatment effects using difference in differences using the pte
package. [Note: This is definitely not the recommended way of doing
this as there is very little error handling, etc. here, but it is rather
a proof of concept. You should use the did package for this case.]
This example reproduces DID estimates of the effect of the minimum wage
on employment using data from the did package.
library(did)
data(mpdta)
did_res <- pte(yname="lemp",
gname="first.treat",
tname="year",
idname="countyreal",
data=mpdta,
setup_pte_fun=setup_pte,
subset_fun=two_by_two_subset,
attgt_fun=did_attgt,
xformla=~lpop)
summary(did_res)
#>
#> Overall ATT:
#> ATT Std. Error [ 95% Conf. Int.]
#> -0.0323 0.0126 -0.0569 -0.0076 *
#>
#>
#> Dynamic Effects:
#> Event Time Estimate Std. Error [95% Conf. Band]
#> -3 0.0269 0.0168 -0.0159 0.0697
#> -2 -0.0050 0.0125 -0.0368 0.0269
#> -1 -0.0229 0.0126 -0.0550 0.0093
#> 0 -0.0201 0.0131 -0.0534 0.0132
#> 1 -0.0547 0.0179 -0.1003 -0.0091 *
#> 2 -0.1382 0.0409 -0.2422 -0.0341 *
#> 3 -0.1069 0.0341 -0.1936 -0.0202 *
#> ---
#> Signif. codes: `*' confidence band does not cover 0
ggpte(did_res)
What’s most interesting here, is that the only “new” code that needs to
be writte is in the did_attgt
function.
You will see that this is a very small amount of code.
Example 2: Policy Evaluation during a Pandemic
As a next example, consider trying to estimate effects of Covid-19 related policies during a pandemic. The estimates below are for the effects of state-leve shelter-in-place orders during the early part of the pandemic.
Callaway and Li (2021) argue that a particular unconfoundedness-type strategy is more appropriate in this context than DID-type strategies due to the spread of Covid-19 cases being highly nonlinear. However, they still deal with the challenge of variation in treatment timing. Therefore, it is still useful to think about group-time average treatment effects, but the DID strategy should be replaced with their particular unconfoundedness type assumption.
The pte package is particularly useful here.
# formula for covariates
xformla <- ~ current + I(current^2) + region + totalTestResults
covid_res <- pte(yname="positive",
gname="group",
tname="time.period",
idname="state_id",
data=covid_data2,
setup_pte_fun=setup_pte_basic,
subset_fun=two_by_two_subset,
attgt_fun=covid_attgt,
xformla=xformla,
max_e=21,
min_e=-10)
#> Warning in compute.aggte(MP = MP, type = type, balance_e = balance_e, min_e =
#> min_e, : Simultaneous conf. band is somehow smaller than pointwise one using
#> normal approximation. Since this is unusual, we are reporting pointwise
#> confidence intervals
summary(covid_res)
#>
#> Overall ATT:
#> ATT Std. Error [ 95% Conf. Int.]
#> 14.8882 58.1647 -99.1125 128.8888
#>
#>
#> Dynamic Effects:
#> Event Time Estimate Std. Error [95% Conf. Band]
#> -10 -3.7266 3.3841 -13.4426 5.9893
#> -9 2.6607 1.5017 -1.6509 6.9722
#> -8 0.8290 2.1465 -5.3339 6.9919
#> -7 5.2843 1.9978 -0.4516 11.0203
#> -6 2.8555 1.8211 -2.3730 8.0841
#> -5 1.3589 4.0879 -10.3778 13.0956
#> -4 0.3294 3.9431 -10.9916 11.6504
#> -3 -4.2227 6.9336 -24.1295 15.6841
#> -2 -3.8447 2.4340 -10.8329 3.1434
#> -1 -0.2234 3.3015 -9.7021 9.2553
#> 0 -10.8156 8.8849 -36.3249 14.6936
#> 1 -13.7998 12.2977 -49.1073 21.5077
#> 2 -7.8432 11.9865 -42.2575 26.5711
#> 3 -4.5541 11.7315 -38.2362 29.1279
#> 4 -3.5368 12.7993 -40.2847 33.2110
#> 5 8.5221 11.1569 -23.5102 40.5544
#> 6 1.1140 17.8634 -50.1732 52.4012
#> 7 6.6384 24.7105 -64.3072 77.5841
#> 8 7.1288 23.8552 -61.3613 75.6189
#> 9 10.8758 30.7177 -77.3170 99.0687
#> 10 17.5057 34.2477 -80.8221 115.8335
#> 11 40.8318 46.6352 -93.0614 174.7250
#> 12 48.6134 57.4288 -116.2692 213.4960
#> 13 52.4228 64.1772 -131.8350 236.6806
#> 14 50.2000 47.2508 -85.4608 185.8608
#> 15 68.2960 66.0155 -121.2395 257.8315
#> 16 44.7305 85.7855 -201.5661 291.0272
#> 17 61.4670 76.4826 -158.1205 281.0544
#> 18 50.4635 114.1011 -277.1294 378.0564
#> 19 47.3392 129.9716 -325.8193 420.4976
#> 20 28.6326 121.7077 -320.7994 378.0646
#> 21 4.3445 133.7291 -379.6020 388.2910
#> ---
#> Signif. codes: `*' confidence band does not cover 0
ggpte(covid_res) + ylim(c(-1000,1000))
What’s most interesting is just how little code needs to be written
here. The only new code required is the ppe::covid_attgt function
which is available
here,
and, as you can see, this is very simple.
Example 3: Empirical Bootstrap
The code above used the multiplier bootstrap. The great thing about the multiplier bootstrap is that it’s fast. But in order to use it, you have to work out the influence function for the estimator of ATT(g,t). Although I pretty much always end up doing this, it can be tedious, and it can be nice to get a working version of the code for a project going before working out the details on the influence function.
The pte package can be used with the empirical bootstrap. There are a
few limitations. First, it’s going to be substantially slower. Second,
this code just reports pointwise confidence intervals. However, this
basically is set up to fit into my typical workflow, and I see this as a
way to get preliminary results.
Let’s demonstrate it. To do this, consider the same setup as in Example 1, but where no influence function is returned. Let’s write the code for this:
# did with no influence function
did_attgt_noif <- function(gt_data, xformla, ...) {
# call original function
did_gt <- did_attgt(gt_data, xformla, ...)
# remove influence function
did_gt$inf_func <- NULL
did_gt
}
Now, we can show the same sorts of results as above
did_res_noif <- pte(yname="lemp",
gname="first.treat",
tname="year",
idname="countyreal",
data=mpdta,
setup_pte_fun=setup_pte,
subset_fun=two_by_two_subset,
attgt_fun=did_attgt_noif, #this is only diff.
xformla=~lpop)
summary(did_res_noif)
#>
#> Overall ATT:
#> ATT Std. Error [ 95% Conf. Int.]
#> -0.0323 0.0107 -0.0533 -0.0113 *
#>
#>
#> Dynamic Effects:
#> Event Time Estimate Std. Error [95% Pointwise Conf. Band]
#> -3 0.0269 0.0151 -0.0026 0.0565
#> -2 -0.0050 0.0115 -0.0275 0.0176
#> -1 -0.0229 0.0153 -0.0529 0.0072
#> 0 -0.0201 0.0102 -0.0400 -0.0002 *
#> 1 -0.0547 0.0172 -0.0885 -0.0210 *
#> 2 -0.1382 0.0391 -0.2148 -0.0616 *
#> 3 -0.1069 0.0379 -0.1813 -0.0325 *
#> ---
#> Signif. codes: `*' confidence band does not cover 0
ggpte(did_res_noif)
What’s exciting about this is just how little new code needs to be written.
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