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)

Arguments

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.

Value

A post-treatment design.

Details

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.

Examples

# 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) # }
# 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)) # }
# 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)) # }

Author

DeclareDesign Team