causalQual_rd {causalQual} | R Documentation |
Causal Inference for Qualitative Outcomes under Regression Discontinuity
Description
Fit local polynomial regression models for qualitative outcomes to estimate the probabilities of shift at the cutoff.
Usage
causalQual_rd(Y, running_variable, cutoff)
Arguments
Y |
Qualitative outcome. Must be labeled as |
running_variable |
Running variable determining treatment assignment. |
cutoff |
Cutoff or threshold. Units with |
Details
Under a regression discontinuity design, identification requires that the probability mass functions for class m
of potential outcomes are continuous in the running variable (continuity). If this assumption holds,
we can recover the probability shift at the cutoff for class m
:
\delta_{m, C} := P(Y_i (1) = m | Running_i = cutoff) - P(Y_i(0) = m | Running_i = cutoff).
causalQual_rd
applies, for each class m
, standard local polynomial estimators to the binary variable 1(Y_i = m)
. Specifically, the ruotine implements the
robust bias-corrected inference procedure of Calonico et al. (2014) (see the rdrobust
function).
Value
An object of class causalQual
.
Author(s)
Riccardo Di Francesco
References
Di Francesco, R., and Mellace, G. (2025). Causal Inference for Qualitative Outcomes. arXiv preprint arXiv:2502.11691. doi:10.48550/arXiv.2502.11691.
See Also
causalQual_soo
causalQual_iv
causalQual_did
Examples
## Generate synthetic data.
set.seed(1986)
data <- generate_qualitative_data_rd(100, outcome_type = "ordered")
Y <- data$Y
running_variable <- data$running_variable
cutoff <- data$cutoff
## Estimate probabilities of shift at the cutoff.
fit <- causalQual_rd(Y, running_variable, cutoff)
summary(fit)
plot(fit)