pte

Generic functions for treatment effects with panel data

https://github.com/bcallaway11/pte

Repository

Generic functions for treatment effects with panel data

README.Rmd

---
output: github_document
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-"
)
```

```{r, echo=FALSE, results="hide", warning=FALSE, message=FALSE}
devtools::load_all("~/Dropbox/pte")
devtools::load_all("~/Dropbox/ppe")
load("~/Dropbox/ppe/data/covid_data.rda")
library(ggplot2)
library(dplyr)
```
# 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](https://github.com/bcallaway11/pte/blob/master/R/subset_functions.R). 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)](https://doi.org/10.1016/j.jeconom.2020.12.001).

Here are a few examples:

## Example 1: Difference in differences

The [`did` package](https://bcallaway11.github.io/did/), which is based on [Callaway and Sant'Anna (2021)](https://doi.org/10.1016/j.jeconom.2020.12.001), 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.

```{r}
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)
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](https://github.com/bcallaway11/pte/blob/master/R/attgt_functions.R).  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)](https://arxiv.org/abs/2105.06927) 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.  

```{r}
# formula for covariates
xformla <- ~ current + I(current^2) + region + totalTestResults
```

```{r echo=FALSE, results=FALSE, warning=FALSE}
# drop some data as in paper
trim_id_list <- lapply(c(10,15,20,25,30),
                       did::trimmer,
                       tname="time.period",
                       idname="state_id",
                       gname="group",
                       xformla=xformla,
                       data=covid_data,
                       control_group="nevertreated",
                       threshold=0.95)
time_id_list <- unlist(trim_id_list)

# unique(subset(covid_data, state_id %in% time_id_list)$state)
covid_data2 <- subset(covid_data, !(state_id %in% time_id_list))
```


```{r}
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) 

summary(covid_res)
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](https://github.com/bcallaway11/ppe/blob/master/R/covid_attgt.R), 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:
```{r}
# 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
```{r}
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)
ggpte(did_res_noif)
```

What's exciting about this is just how little new code needs to be written.  

Owner

Citation (CITATION.cff)

# --------------------------------------------
# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# --------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "pte" in publications use:'
type: software
license: GPL-3.0-only
title: 'pte: Panel Treatment Effects'
version: 0.0.0.9000
abstract: This is fairly generic code for estimating treatment effects with panel
  data. This is useful code in my own work and will perhaps be useful to others. It
  builds heavily on the \codedid package. In particular, it breaks into separate steps
  organizing the data, looping over groups and time periods, computing group-time
  average treatment effects, and aggregating group-time average treatment effects.
  Often, one is able to implement a new identification/estimation procedure by simply
  replacing the step on estimating group-time average treatment effects. See several
  different examples of this approach in the README.
authors:
- family-names: Callaway
  given-names: Brantly
  email: brantly.callaway@uga.edu
repository-code: https://github.com/bcallaway11/pte
url: https://github.com/bcallaway11/pte
contact:
- family-names: Callaway
  given-names: Brantly
  email: brantly.callaway@uga.edu
references:
- type: software
  title: BMisc
  abstract: 'BMisc: Miscellaneous Functions for Panel Data, Quantiles, and Printing
    Results'
  notes: Imports
  url: https://bcallaway11.github.io/BMisc/
  repository: https://CRAN.R-project.org/package=BMisc
  authors:
  - family-names: Callaway
    given-names: Brantly
    email: brantly.callaway@uga.edu
  year: '2025'
  doi: 10.32614/CRAN.package.BMisc
  version: '>= 1.4.7'
- type: software
  title: Matrix
  abstract: 'Matrix: Sparse and Dense Matrix Classes and Methods'
  notes: Imports
  url: https://R-forge.R-project.org/tracker/?atid=294&group_id=61
  repository: https://CRAN.R-project.org/package=Matrix
  authors:
  - family-names: Bates
    given-names: Douglas
    orcid: https://orcid.org/0000-0001-8316-9503
  - family-names: Maechler
    given-names: Martin
    email: mmaechler+Matrix@gmail.com
    orcid: https://orcid.org/0000-0002-8685-9910
  - family-names: Jagan
    given-names: Mikael
    orcid: https://orcid.org/0000-0002-3542-2938
  year: '2025'
  doi: 10.32614/CRAN.package.Matrix
- type: software
  title: did
  abstract: 'did: Treatment Effects with Multiple Periods and Groups'
  notes: Imports
  url: https://bcallaway11.github.io/did/
  repository: https://CRAN.R-project.org/package=did
  authors:
  - family-names: Callaway
    given-names: Brantly
    email: brantly.callaway@uga.edu
  - family-names: Sant'Anna
    given-names: Pedro H. C.
    email: pedro.h.santanna@vanderbilt.edu
  year: '2025'
  doi: 10.32614/CRAN.package.did
  version: '>= 2.0.0'
- type: software
  title: ggplot2
  abstract: 'ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics'
  notes: Imports
  url: https://ggplot2.tidyverse.org
  repository: https://CRAN.R-project.org/package=ggplot2
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
    orcid: https://orcid.org/0000-0003-4757-117X
  - family-names: Chang
    given-names: Winston
    orcid: https://orcid.org/0000-0002-1576-2126
  - family-names: Henry
    given-names: Lionel
  - family-names: Pedersen
    given-names: Thomas Lin
    email: thomas.pedersen@posit.co
    orcid: https://orcid.org/0000-0002-5147-4711
  - family-names: Takahashi
    given-names: Kohske
  - family-names: Wilke
    given-names: Claus
    orcid: https://orcid.org/0000-0002-7470-9261
  - family-names: Woo
    given-names: Kara
    orcid: https://orcid.org/0000-0002-5125-4188
  - family-names: Yutani
    given-names: Hiroaki
    orcid: https://orcid.org/0000-0002-3385-7233
  - family-names: Dunnington
    given-names: Dewey
    orcid: https://orcid.org/0000-0002-9415-4582
  - family-names: Brand
    given-names: Teun
    name-particle: van den
    orcid: https://orcid.org/0000-0002-9335-7468
  year: '2025'
  doi: 10.32614/CRAN.package.ggplot2
- type: software
  title: DRDID
  abstract: 'DRDID: Doubly Robust Difference-in-Differences Estimators'
  notes: Imports
  url: https://psantanna.com/DRDID/
  repository: https://CRAN.R-project.org/package=DRDID
  authors:
  - family-names: Sant'Anna
    given-names: Pedro H. C.
    email: pedrosantanna@causal-solutions.com
  - family-names: Zhao
    given-names: Jun
    email: beanzhaojun11@gmail.com
  year: '2025'
  doi: 10.32614/CRAN.package.DRDID
- type: software
  title: tidyr
  abstract: 'tidyr: Tidy Messy Data'
  notes: Imports
  url: https://tidyr.tidyverse.org
  repository: https://CRAN.R-project.org/package=tidyr
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  - family-names: Vaughan
    given-names: Davis
    email: davis@posit.co
  - family-names: Girlich
    given-names: Maximilian
  year: '2025'
  doi: 10.32614/CRAN.package.tidyr
- type: software
  title: dplyr
  abstract: 'dplyr: A Grammar of Data Manipulation'
  notes: Imports
  url: https://dplyr.tidyverse.org
  repository: https://CRAN.R-project.org/package=dplyr
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
    orcid: https://orcid.org/0000-0003-4757-117X
  - family-names: François
    given-names: Romain
    orcid: https://orcid.org/0000-0002-2444-4226
  - family-names: Henry
    given-names: Lionel
  - family-names: Müller
    given-names: Kirill
    orcid: https://orcid.org/0000-0002-1416-3412
  - family-names: Vaughan
    given-names: Davis
    email: davis@posit.co
    orcid: https://orcid.org/0000-0003-4777-038X
  year: '2025'
  doi: 10.32614/CRAN.package.dplyr
- type: software
  title: pbapply
  abstract: 'pbapply: Adding Progress Bar to ''*apply'' Functions'
  notes: Imports
  url: https://github.com/psolymos/pbapply
  repository: https://CRAN.R-project.org/package=pbapply
  authors:
  - family-names: Solymos
    given-names: Peter
    email: psolymos@gmail.com
    orcid: https://orcid.org/0000-0001-7337-1740
  - family-names: Zawadzki
    given-names: Zygmunt
    email: zygmunt@zstat.pl
  year: '2025'
  doi: 10.32614/CRAN.package.pbapply
- type: software
  title: testthat
  abstract: 'testthat: Unit Testing for R'
  notes: Suggests
  url: https://testthat.r-lib.org
  repository: https://CRAN.R-project.org/package=testthat
  authors:
  - family-names: Wickham
    given-names: Hadley
    email: hadley@posit.co
  year: '2025'
  doi: 10.32614/CRAN.package.testthat
  version: '>= 3.0.0'

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