README.md
In qte: Quantile Treatment Effects

qte — Quantile Treatment Effects in R

The qte package provides methods for estimating Quantile Treatment Effects (QTE) and Quantile Treatment Effects on the Treated (QTT) in R. Where the average treatment effect summarizes the impact of a policy by a single number, the QTE describes how treatment effects vary across the outcome distribution — useful whenever the policy’s impact is heterogeneous or when distributional consequences (e.g., for inequality) are of interest.

Cross-sectional estimators (no panel data required):

unc_qte() — QTE/QTT under unconfoundedness (IPW, outcome regression, or doubly robust); covers random assignment as a special case

Panel and repeated cross-section estimators (staggered treatment adoption supported for all):

cic() — Change in Changes (Athey and Imbens 2006)
qdid() — Quantile Difference-in-Differences (Athey and Imbens 2006; Meyer, Viscusi, and Durbin 1995)
panel_qtt() — Panel QTT via copula stability (Callaway and Li 2019)
ddid() — Distributional Difference-in-Differences (Callaway and Li 2019)
mdid() — Mean Difference-in-Differences (Thuysbaert 2007)
lou_qtt() — Lagged-outcome unconfoundedness QTT

# Install from CRAN:
install.packages("qte")

# Install the development version from GitHub:
# install.packages("remotes")
remotes::install_github("bcallaway11/qte")

The unc_qte() function estimates the QTE or QTT under an unconfoundedness assumption. Here we use the observational Lalonde (1986) data to estimate the QTT of a job training program, controlling for pre-treatment characteristics via doubly robust estimation.

data(lalonde)

xf <- ~ age + I(age^2) + education + black + hispanic + married + nodegree

res_cs <- unc_qte(
  yname      = "re78",
  dname      = "treat",
  data       = lalonde.psid,
  xformla    = xf,
  est_method = "aipw",
  target     = "qtt",
  probs      = seq(0.1, 0.9, 0.1),
  biters     = 100
)
summary(res_cs)
#> 
#> Overall ATT:  
#>        ATT    Std. Error     [ 95%  Conf. Int.]  
#>  -4685.583      856.1013  -6363.511   -3007.655 *
#> 
#> 
#> QTT:
#>  Tau         QTT Std. Error [ 95% Simult.  Conf. Band]  
#>  0.1      0.0001    30.0232       -58.8443     58.8444  
#>  0.2  -1002.7420   688.8295     -2352.8229    347.3389  
#>  0.3  -3400.5673  1731.1817     -6793.6212     -7.5135 *
#>  0.4  -5009.2491  1181.6380     -7325.2170  -2693.2811 *
#>  0.5  -4602.4652   848.2877     -6265.0786  -2939.8519 *
#>  0.6  -5229.1454  1230.4344     -7640.7526  -2817.5383 *
#>  0.7  -5507.4720  1199.7046     -7858.8498  -3156.0942 *
#>  0.8  -6885.7529  1376.9064     -9584.4399  -4187.0659 *
#>  0.9 -10517.0625  2373.8362    -15169.6961  -5864.4290 *
#> ---
#> Signif. codes: `*' confidence band does not cover 0

Plot the QTT curve with a uniform confidence band:

autoplot(res_cs)

All panel estimators use a common yname/gname/tname/idname interface and support staggered treatment adoption via ptetools. The example below uses the mpdta dataset (county-level employment, from the did package) with the Change in Changes estimator.

data(mpdta, package = "did")

res_att <- cic(
  yname   = "lemp",
  gname   = "first.treat",
  tname   = "year",
  idname  = "countyreal",
  data    = mpdta,
  gt_type = "att",
  biters  = 100
)
summary(res_att)
#> 
#> Overall ATT:  
#>      ATT    Std. Error     [ 95%  Conf. Int.] 
#>  -0.0197         0.018    -0.0617      0.0224 
#> 
#> 
#> Dynamic Effects:
#>  Event Time Estimate Std. Error [95% Simult.  Conf. Band]  
#>          -3   0.0508     0.0222        0.0074      0.0943 *
#>          -2   0.0158     0.0147       -0.0130      0.0447  
#>          -1  -0.0128     0.0165       -0.0452      0.0196  
#>           0  -0.0081     0.0171       -0.0416      0.0255  
#>           1  -0.0364     0.0233       -0.0820      0.0092  
#>           2  -0.1226     0.0443       -0.2093     -0.0358 *
#>           3  -0.0930     0.0473       -0.1857     -0.0002 *
#> ---
#> Signif. codes: `*' confidence band does not cover 0

Event-study plot showing pre-trends and post-treatment ATT by event time:

autoplot(res_att, type = "dynamic")

The same estimator returns a full QTT curve when gt_type = "qtt":

res_qtt <- cic(
  yname   = "lemp",
  gname   = "first.treat",
  tname   = "year",
  idname  = "countyreal",
  data    = mpdta,
  gt_type = "qtt",
  probs   = seq(0.1, 0.9, 0.1),
  biters  = 100
)
autoplot(res_qtt)

| Function | Method | Target | Panel required | |---------------|------------------------------------|------------|----------------| | unc_qte() | Unconfoundedness (IPW / OR / AIPW) | QTE or QTT | No | | cic() | Change in Changes | ATT or QTT | Optional | | qdid() | Quantile DiD | ATT or QTT | Optional | | panel_qtt() | Panel QTT (copula stability) | QTT | Yes | | ddid() | Distributional DiD | ATT or QTT | Yes | | mdid() | Mean DiD | ATT or QTT | Optional | | lou_qtt() | Lagged-outcome unconfoundedness | ATT or QTT | Yes |

All panel estimators support staggered treatment adoption and return group-specific, event-study, and overall aggregations.

Full documentation and vignettes are available at the pkgdown site:

Quantile Treatment Effects in R — unc_qte() under random assignment and selection on observables
Panel Data Estimators for Quantile Treatment Effects — identification assumptions and usage for all six panel estimators
Staggered Treatment Adoption — applied workflow with mpdta: QTT curves, event-study plots, and cross-estimator comparison