run.comparison = function(XYW) {
X = XYW$X
Y = XYW$Y
W = XYW$W
# Run residual balancing
tau.rb = residualBalance.ate(X, Y, W, target.pop = 1, fit.method = "elnet", alpha = 0.9, zeta = 0.5)
tau.rb.plain = residualBalance.ate(X, Y, W, target.pop = 1, fit.method = "none", zeta = 0.5)
# Run inverse-propensity weighted methods
tau.ipw.elnet = ipw.ate(X, Y, W, target.pop = 1, fit.method = "none", prop.method = "elnet", alpha.prop = 0.5)
tau.aipw = ipw.ate(X, Y, W, target.pop = 1, prop.weighted.fit = FALSE, targeting.method = "AIPW",
fit.method = "elnet", prop.method = "elnet", alpha.fit = 0.9, alpha.prop = 0.5)
tau.ipw.weighted = ipw.ate(X, Y, W, target.pop = 1, prop.weighted.fit = TRUE, targeting.method = "AIPW",
fit.method = "elnet", prop.method = "elnet", alpha.fit = 0.9, alpha.prop = 0.5)
tau.tmle = ipw.ate(X, Y, W, prop.weighted.fit = FALSE, targeting.method = "TMLE",
target.pop = 1, fit.method = "elnet", prop.method = "elnet", alpha.fit = 0.9, alpha.prop = 0.5)
# Run other baselines
tau.naive = naive.ate(X, Y, W)
tau.elnet = elnet.ate(X, Y, W, target.pop = 1, alpha = 0.9)
tau.twostep = twostep.lasso.ate(X, Y, W, target.pop = 1)
results = c(Naive=tau.naive,
Elnet=tau.elnet,
Twostep = tau.twostep,
ResidBalance=tau.rb,
ResidBalancePlain=tau.rb.plain,
IPW.Elnet = tau.ipw.elnet,
IPW.Residual = tau.aipw,
IPW.Weighted = tau.ipw.weighted,
TMLE = tau.tmle)
results
}
coverage.test = function(XYW) {
X = XYW$X
Y = XYW$Y
W = XYW$W
# Run residual balancing
tau.rb = residualBalance.ate(X, Y, W, target.pop = 1, fit.method = "elnet", alpha = 0.9, zeta = 0.5, estimate.se = TRUE)
tau.rb
}
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