make_exposure_map_AS: Create treatment exposure conditions.
In szonszein/interference: Design-Based Estimation of Spillover Effects

Description Usage Arguments Details Value Functions References Examples

Create treatment exposure conditions according to selected exposure mappings.

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make_exposure_map_AS(adj_matrix, tr_vector, hop)

make_exposure_map_full_neighborhood(adj_matrix, tr_vector)

`adj_matrix`	an `N` * `N` numeric matrix of 0, 1 entries such as those retuned by `make_adj_matrix`, or a `N` * `N` matrix, where `N` is the number of units.
`tr_vector`	a numeric vector of length `N` of 0, 1 entries such as that retuned by `make_tr_vec_permutation` with `R = 1`, or a vector of length `N` with the treatment assignment.
`hop`	number; either `1` or `2`. If `1` assumes first-degree interference and produces four exposure conditions, if `2` assumes second-degree interference and produces eight exposure conditions.

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 (first-degree interference) which produces four exposure conditions: Direct + Indirect Exposure, Isolated Direct Exposure, Indirect Exposure, No Exposure. And an exposure mapping that assumes second-degree 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 first-degree interference and that produces two exposure conditions: All Treat; a unit and 100% of its first-degree neighbors are treated, and All Control; a unit and 100% of its first-degree 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.

# 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)