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.