make_tr_condition: Create treatment conditions under a two-stage hierarchical...
In szonszein/interference: Design-Based Estimation of Spillover Effects

Description Usage Arguments Details Value References Examples

Create treatment conditions under two-stage hierarchical treatment assignment, assuming partial and stratified interference.

1	make_tr_condition(tr_assignment)

tr_assignment

data frame of N observations and the variables:

group_tr:: Indicator of group assignment to high saturation (psi) in the first stage.
indiv_tr:: Indicator of individual assignment to treatment in the second stage.

This data frame is returned by function make_tr_vec_permutation_hierarchical.

make_tr_condition produces treatment conditions according to a two-stage hierarchical design where groups are first randomly assigned to a high or a low level of treatment saturation (psi, phi), and then units within groups are randomly assigned to treatment with probability equal to their group saturation rate. Following Hudgens and Halloran (2008), there are four potential outcomes which correspond to the four potential treatment conditions: Direct + Indirect Psi–a unit is directly treated and its group is assigned treatment saturation psi, Direct + Indirect Phi–a unit is directly treated and its group is assigned treatment saturation phi, Indirect Psi–a unit is not directly treated and its group is assigned treatment saturation psi, Indirect Phi–a unit is not directly treated and its group is assigned treatment saturation phi. This function assumes stratified interference (i.e. potential outcomes of a unit are affected by its own treatment assignment and only the treated proportion of its group; the precise set of treated group members does not matter).

named numeric N * K matrix of observed treatment conditions K for units N.

Hudgens, M.G. & Halloran M.E. (2008). Toward causal inference with interference. Journal of the American Statistical Association, 103(482), 832–842.

Aronow, P.M. et al. (2020). Spillover effects in experimental data. arXiv preprint, arXiv:2001.05444.

# Create data frame with first and second stage
# treatment assignment indicators:

group <- rep(1:6, each = 30/6)
c <- 1/2
k <- c(2/5, 3/5)

tr_assignment <-
make_tr_vec_permutation_hierarchical(group, c, k, R = 1,
                                     seed = 357)[[1]]

# Create data frame with observed treatment conditions:
make_tr_condition(tr_assignment)