Description Usage Arguments Details Value Functions References Examples
View source: R/make_exposure_map_AS.R
Create treatment exposure conditions according to selected exposure mappings.
1 2 3  make_exposure_map_AS(adj_matrix, tr_vector, hop)
make_exposure_map_full_neighborhood(adj_matrix, tr_vector)

adj_matrix 
an 
tr_vector 
a numeric vector of length 
hop 
number; either 
make_exposure_map_AS
produces treatment exposure conditions according
to two different exposure mappings. An exposure mapping that assumes that
interference happens only through direct peer connections (firstdegree
interference) which produces four exposure conditions: Direct + Indirect
Exposure, Isolated Direct Exposure, Indirect Exposure, No
Exposure. And an exposure mapping that assumes seconddegree interference
which produces eight exposure conditions.
An N
* K numeric matrix, where K corresponds to the number of
exposure conditions.
make_exposure_map_full_neighborhood
: Create treatment exposure conditions
according to an exposure mapping that assumes firstdegree interference and
that produces two exposure conditions: All Treat; a unit and 100% of
its firstdegree neighbors are treated, and All Control; a unit and
100% of its firstdegree neighbors are in the control condition.
Aronow, P.M. & Samii, C. (2017). Estimating average causal effects under general interference, with application to a social network experiment. The Annals of Applied Statistics, 11(4), 1912–1947.
Aronow, P.M. et al. (2020). Spillover effects in experimental data. arXiv preprint, arXiv:2001.05444.
1 2 3 4 5 6 7 8 9 10 11  # Create an adjacency matrix and a treatment vector:
adj_matrix < make_adj_matrix(N = 9, model = 'sq_lattice')
tr_vector < make_tr_vec_permutation(N = 9, p = 0.2, R = 1,
seed = 357)
# Create treatment exposure conditions:
make_exposure_map_AS(adj_matrix, tr_vector, hop = 1)
make_exposure_map_AS(adj_matrix, tr_vector, hop = 2)
make_exposure_map_full_neighborhood(adj_matrix, tr_vector)

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