Computes the large sample confidence intervals of Liu and Hudgens (2014) for treatment effects on a binary outcome in a twostage randomized experiment with interference
1 
eff 
treatment effect of interest; either “DEa0”, “DEa1”, “IE”, “TE”, or “OE” 
g 
1st stage of randomization vector where element i=1,…,k is equal to 1 if group i was randomized to strategy α_{1} and 0 if randomized to strategy α_{0} 
data 
2 \times 2\times k array of 2 \times 2 table data where row 1 is treatment=yes, row 2 is treatment=no, column 1 is outcome=yes, and column 2 is outcome=no 
m.a0 
α_{0} randomization vector where element i=1,…,k is equal to the number of subjects in group i who would receive treatment if group i was randomized to strategy α_{0} 
m.a1 
α_{1} randomization vector where element i=1,…,k is equal to the number of subjects in group i who would receive treatment if group i was randomized to strategy α_{1} 
level 
significance level, i.e., method yields a 1level confidence interval 
est 
estimated treatment effect 
v 
estimated variance 
lower.w 
lower limit to Wald confidence interval 
upper.w 
upper limit to Wald confidence interval 
lower.ch 
lower limit to Chebyshev confidence interval 
upper.ch 
upper limit to Chebyshev confidence interval 
Joseph Rigdon jrigdon@bios.unc.edu
Hudgens, M.G. and Halloran, M.E. “Toward causal inference with interference.” Journal of the American Statistical Association 2008 103:832842.
Liu, L. and Hudgens, M.G. “Large sample randomization inference of causal effects in the presence of interference.” Journal of the American Statistical Association 2014 109:288301.
1 2 3 4 5 6 7  #Table 3 from Hudgens and Halloran (2008)
hh = array(c(16,18,1254116,1254118,26,54,1151326,1151354,17,119,1077217,
25134119,22,122,888322,20727122,15,92,562715,1313092),c(2,2,5))
e1 = HH('OE',c(1,1,0,0,0),hh,round(0.3*c(25082,23026,35906,29610,18757),0),
round(0.5*c(25082,23026,35906,29610,18757),0),0.05)
round(1000*e1$est,3)
round(1000000*e1$v,3)

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