persuasio estimates and bounds persuasion effects in
instrumental variable settings with binary outcomes. You provide the
outcome, the treatment, and the instrument, tell persuasio
which estimand you want (average or local persuasion rate), and it takes
care of the bounds and inference. Based on Jun and Lee (2023) https://doi.org/10.1086/724114.
You can install the development version of persuasio from GitHub with:
# install.packages("pak")
pak::pak("persuasio/persuasio-r")The original Stata implementation is available at https://github.com/persuasio/persuasio-stata and from
SSC as persuasio.
library(persuasio)
## basic example code
# Average persuasion rate (APR): normal inference
persuasio(
est = "apr",
y = "voteddem_all",
t = "readsome",
z = "post",
data = GKB,
level = 0.80,
method = "normal"
)
#>
#> Average persuasion rate for binary outcomes, binary treatments and binary instruments
#>
#> Outcome: voteddem_all
#> Treatment: readsome
#> Instrument: post
#> Model: no_interaction
#> Method: normal
#> Observations: 701
#>
#> Estimates:
#> Lower Bound Upper Bound CI Lower CI Upper
#> 0.0707 0.6343 0.0288 0.6611
#>
#> Confidence level: 80%
# Local persuasion rate (LPR): bootstrap inference
persuasio(
est = "lpr",
y = "voteddem_all",
t = "readsome",
z = "post",
data = GKB,
level = 0.80,
method = "bootstrap",
nboot = 1000
)
#>
#> Local persuasion rate for binary outcomes, binary treatments and binary instruments
#>
#> Outcome: voteddem_all
#> Treatment: readsome
#> Instrument: post
#> Model: no_interaction
#> Method: bootstrap
#> Observations: 701
#>
#> Estimates:
#> LPR CI Lower CI Upper
#> 0.8067 0.0664 1
#>
#> Confidence level: 80%
#> Bootstrap replications: 1000See vignette("getting-started", package = "persuasio")
for a full walkthrough including covariates, model specifications, and
the relationship between estimands.
Jun, Sung Jae, and Sokbae Lee. 2023. “Identifying the Effect of Persuasion.” Journal of Political Economy 131 (8): 2032-2058. https://doi.org/10.1086/724114.