Simulated experiment whose effect varies with a continuous covariate
continuous_interaction.RdThe treatment effect is a nonlinear function of X, which makes this dataset
useful for showing conditional average treatment effects. Subjects were also
sampled with probability related to X, so the sample is not a simple random
sample of the population that generated it.
Format
continuous_interaction
A tibble with 1189 rows and 10 columns:
- ID
Subject identifier
- noise
Unobserved subject-level shock
- X
Continuous pretreatment covariate
- Y_Z_1, Y_Z_0
Continuous potential outcomes under treatment and control
- S_inclusion_prob
Probability the subject was sampled, which increases in
X- Z
Random assignment, 1 for treatment and 0 for control
- Z_cond_prob
Probability of the assigned condition, 0.5 throughout
- Y
Revealed outcome
- condition
Zlabelled "Treatment" and "Control"