Description Usage Arguments Details Value References Examples
Create matrices of exposure probabilities.
1 2 3 4 5 6  make_exposure_prob(
potential_tr_vector,
adj_matrix,
exposure_map_fn,
exposure_map_fn_add_args = NULL
)

potential_tr_vector 
an 
adj_matrix 
an 
exposure_map_fn 
function which returns the exposure mapping such as

exposure_map_fn_add_args 
list of additional arguments which are passed
to 
make_exposure_prob
produces the units' probabilities of being subject
to each of the possible exposure conditions which are used for estimating
exposurespecific causal effects, and the joint exposure probabilities used
for variance estimators.
A list of 3 lists:
I_exposure
:A list of K
N
* R
numeric matrices of indicators for whether units N
are in
exposure condition k over each of the possible R
treatment assignment
vectors. The number of numeric matrices K corresponds to the number of
exposure conditions.
prob_exposure_k_k
:A list of K symmetric
N
* N
numeric matrices each containing individual exposure probabilities
to condition k on the diagonal, and joint exposure probabilities to
condition k on the offdiagonals.
prob_exposure_k_l
:A list of
permutation(K,2) nonsymmetric N
* N
numeric matrices each
containing joint probabilities across exposure conditions k and l on the
offdiagonal, and zeroes on the diagonal. When K = 4, the number of numeric
matrices is 12; permutation(4,2).
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  potential_tr_vector < make_tr_vec_permutation(N = 9, p = 0.2, R = 20, seed = 357)
adj_matrix < make_adj_matrix(N = 9, model = 'sq_lattice')
make_exposure_prob(potential_tr_vector, adj_matrix, make_exposure_map_AS, list(hop=1))

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