two_arm_attrition_designer {DesignLibrary} | R Documentation |
Creates a two-arm design with application for when estimand of interest is conditional on a post-treatment outcome (the effect on Y given R) or data is conditionally observed (Y given R). See 'Details' for more information on the data generating process.
two_arm_attrition_designer(
N = 100,
a_R = 0,
b_R = 1,
a_Y = 0,
b_Y = 1,
rho = 0,
args_to_fix = NULL
)
N |
An integer. Size of sample. |
a_R |
A number. Constant in equation relating treatment to responses. |
b_R |
A number. Slope coefficient in equation relating treatment to responses. |
a_Y |
A number. Constant in equation relating treatment to outcome. |
b_Y |
A number. Slope coefficient in equation relating treatment to outcome. |
rho |
A number in [0,1]. Correlation between shocks in equations for R and Y. |
args_to_fix |
A character vector. Names of arguments to be args_to_fix in design. |
The data generating process is of the form:
R ~ (a_R + b_R*Z > u_R)
Y ~ (a_Y + b_Y*Z > u_Y)
where u_R
and u_Y
are joint normally distributed with correlation rho
.
A post-treatment design.
# To make a design using default argument (missing completely at random):
two_arm_attrition_design <- two_arm_attrition_designer()
## Not run:
diagnose_design(two_arm_attrition_design)
## End(Not run)
# Attrition can produce bias even for unconditional ATE even when not
# associated with treatment
## Not run:
diagnose_design(two_arm_attrition_designer(b_R = 0, rho = .3))
## End(Not run)
# Here the linear estimate using R=1 data is unbiased for
# "ATE on Y (Given R)" with b_R = 0 but not when b_R = 1
## Not run:
diagnose_design(redesign(two_arm_attrition_design, b_R = 0:1, rho = .2))
## End(Not run)