library(geex)
##
## Attaching package: 'geex'
## The following object is masked from 'package:methods':
##
##     show
library(inferference)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
##     filter, lag
## The following objects are masked from 'package:base':
##
##     intersect, setdiff, setequal, union

## Estimating a propensity score vs. treating propensity as known

TODO: describe what’s going on here

n <- 1000
x <- data_frame(
A  = rbinom(n, 1, .2),
Y0 = rnorm(n, 0, 1),
Y1 = rnorm(n, 2 * A, 1),
Y = (A*Y1) + (1 - A)*Y0)
ipw_estFUN <- function(data){
A <- data$A Y <- data$Y
function(theta, phat){
ipw0 <- 1/theta[1]
ipw1 <- 1/theta[2]

# Estimating functions #
c( (1 - A) - theta[1],
A - theta[2],

# Estimating IP weight
Y*(1 - A)*ipw0 - theta[3],
Y*(A)*ipw1 - theta[4],

# Treating IP weight as known
Y*A/phat - theta[5]
)
}
}
phat <- mean(x$A) out <- m_estimate(ipw_estFUN, data = x, inner_args = list(phat = phat), root_control = setup_root_control(start = c(.5, .5, 0, 0, 0))) ## Comparing point estimates all.equal(mean(x$Y * x$A/phat), coef(out)[4])  ## [1] TRUE all.equal(phat, coef(out)[2])  ## [1] TRUE ## Comparing variance estimates geex_vcov <- diag(vcov(out)) * n # estimates match treating propensity as known all.equal(var(x$Y * x$A/phat) * (n - 1)/n, geex_vcov[5]) ## [1] TRUE # estimates match using influence function approach y <- x$Y * x$A/phat - mean(x$Y * x$A/phat) z <- (x$A - phat) / (phat*(1 - phat))
var(y - predict(lm(y ~ z))) - geex_vcov[4] # close
## [1] 0.00485138

## IPW estimator of counterfactual mean

An example $$\psi$$ function written in R. This function computes the score functions for a GLM, plus two counterfactual means estimated by inverse probability weighting.

eefun <- function(data, model, alpha){
X <- model.matrix(model, data = data)
A <- model.response(model.frame(model, data = data))
Y <- data$Y function(theta){ p <- length(theta) p1 <- length(coef(model)) lp <- X %*% theta[1:p1] rho <- plogis(lp) hh <- ((rho/alpha)^A * ((1-rho)/(1-alpha))^(1 - A)) IPW <- 1/(exp(sum(log(hh)))) score_eqns <- apply(X, 2, function(x) sum((A - rho) * x)) ce0 <- mean(Y * (A == 0)) * IPW / (1 - alpha) ce1 <- mean(Y * (A == 1)) * IPW / (alpha) c(score_eqns, ce0 - theta[p - 1], ce1 - theta[p]) } } Compare to what inferference gets. test <- interference( formula = Y | A ~ X1 | group, data = vaccinesim, model_method = 'glm', allocations = c(.35, .4)) mglm <- glm(A ~ X1, data = vaccinesim, family = binomial) ce_estimates <- m_estimate( estFUN = eefun, data = vaccinesim, units = 'group', root_control = setup_root_control(start = c(coef(mglm), .4, .13)), outer_args = list(alpha = .35, model = mglm) ) roots(ce_estimates) ## (Intercept) X1 ## -0.36869683 -0.02037916 0.42186669 0.15507946 # Compare parameter estimates direct_effect(test, allocation = .35)$estimate
## [1] 0.2667872
roots(ce_estimates)[3] - roots(ce_estimates)[4]
##
## 0.2667872
# conpare SE estimates
L <- c(0, 0, 1, -1)
Sigma <- vcov(ce_estimates)
sqrt(t(L) %*% Sigma %*% L)  # from GEEX
##            [,1]
## [1,] 0.03716025
direct_effect(test, allocation = .35)$std.error # from inferference ## [1] 0.02602316 I would expect them to be somewhat different, since inferference uses a slightly different variance estimator defined in the web appendix of Perez-Heydrich et al (2014). ## Doubly-Robust Estimator Estimators of causal effects often have the form: $$$\label{eq:causal} \sum_{i = 1}^m \psi(O_i, \theta) = \sum_{i = 1}^m \begin{pmatrix} \psi(O_i, \nu) \\ \psi(O_i, \beta) \end{pmatrix} = 0,$$$ where $$\nu$$ are parameters in nuisance model(s), such as a propensity score model, and $$\beta$$ are the target causal parameters. Even when $$\nu$$ represent parameters in common statistical models, deriving a closed form for a sandwich variance estimator for $$\beta$$ based on Equation~ may involve tedious and error-prone derivative and matrix calculations (e.g.; see the appendices of Lunceford and Davidian 2004, and @perez–heydrich2014assessing). In this example, we show how an analyst can avoid these calculations and compute the empirical sandwich variance estimator using geex. Lunceford and Davidian (2004) review several estimators of causal effects from observational data. To demonstrate a more complicated estimator involving multiple nuisance models, we implement the doubly robust estimator: $$$\label{eq:dbr} \hat{\Delta}_{DR} = \sum_{i = 1}^m \frac{Z_iY_i - (Z_i - \hat{e}_i) m_1(X_i, \hat{\alpha}_1)}{\hat{e}_i} - \frac{(1 - Z_i)Y_i - (Z_i - \hat{e}_i) m_0(X_i, \hat{\alpha}_0)}{1 - \hat{e}_i}.$$$ This estimator targets the average causal effect, $$\Delta = \E[Y(1) - Y(0)]$$, where $$Y(z)$$ is the potential outcome for an experimental unit had it been exposed to the level $$z$$ of the binary exposure variable $$Z$$. The estimated propsensity score, $$\hat{e}_i$$, is the estimated probability that unit $$i$$ received $$z = 1$$ and $$m_z(X_i, \hat{\alpha}_z)$$ are regression models for the outcome with baseline covariates $$X_i$$ and estimated paramaters $$\hat{\alpha}_z$$. This estimator has the property that if either the propensity score models or the outcome models are correctly specified, then the solution to Equation~ will be a consistent and asymptotically Normal estimator of $$\Delta$$. This estimator and its estimating equations can be translated into an estFUN as: dr_estFUN <- function(data, models){ Z <- data$Z
Y <- data$Y Xe <- grab_design_matrix( data, rhs_formula = grab_fixed_formula(models$e))
Xm0 <- grab_design_matrix(
data, rhs_formula = grab_fixed_formula(models$m0)) Xm1 <- grab_design_matrix( data, rhs_formula = grab_fixed_formula(models$m1))

e_pos  <- 1:ncol(Xe)
m0_pos <- (max(e_pos) + 1):(max(e_pos) + ncol(Xm0))
m1_pos <- (max(m0_pos) + 1):(max(m0_pos) + ncol(Xm1))

e_scores  <- grab_psiFUN(models$e, data) m0_scores <- grab_psiFUN(models$m0, data)
m1_scores <- grab_psiFUN(models\$m1, data)

function(theta){
e  <- plogis(Xe %*% theta[e_pos])
m0 <- Xm0 %*% theta[m0_pos]
m1 <- Xm1 %*% theta[m1_pos]
rd_hat <- (Z*Y - (Z - e) * m1)/e - ((1 - Z) * Y - (Z - e) * m0)/(1 - e)
c(e_scores(theta[e_pos]),
m0_scores(theta[m0_pos]) * (Z == 0),
m1_scores(theta[m1_pos]) * (Z == 1),
rd_hat - theta[length(theta)])
}
}

This estFUN presumes that the user will pass a list containing fitted model objects for the three nuisance models: the propensity score model and one regression model for each treatment group. The functions grab_design_matrix and grab_fixed_formula are geex utilities for extracting relevant pieces of a model object. The function grab_psiFUN converts a fitted model object to an estimating function; for example, for a glm object, grab_psiFUN uses the to create a function of theta corresponding to the generalized linear model score function. The m_estimate function can be wrapped in another function, wherein nuisance models are fit and passed to m_estimate.

estimate_dr <- function(data, propensity_formula, outcome_formula){
e_model  <- glm(propensity_formula, data = data, family = binomial)
m0_model <- glm(outcome_formula, subset = (Z == 0), data = data)
m1_model <- glm(outcome_formula, subset = (Z == 1), data = data)
models <- list(e = e_model, m0 = m0_model, m1 = m1_model)
nparms <- sum(unlist(lapply(models, function(x) length(coef(x))))) + 1

m_estimate(
estFUN = dr_estFUN,
data   = data,
root_control = setup_root_control(start = rep(0, nparms)),
outer_args = list(models = models))
}

The following code provides a function to replicate the simulation settings of Lunceford and Davidian (2004).

library(mvtnorm)
tau_0 <- c(-1, -1, 1, 1)
tau_1 <- tau_0 * -1
Sigma_X3 <- matrix(
c(1, 0.5, -0.5, -0.5,
0.5, 1, -0.5, -0.5,
-0.5, -0.5, 1, 0.5,
-0.5, -0.5, 0.5, 1), ncol = 4, byrow = TRUE)

gen_data <- function(n, beta, nu, xi){
X3 <- rbinom(n, 1, prob = 0.2)
V3 <- rbinom(n, 1, prob = (0.75 * X3 + (0.25 * (1 - X3))))
hold <- rmvnorm(n,  mean = rep(0, 4), Sigma_X3)
colnames(hold) <- c("X1", "V1", "X2", "V2")
hold <- cbind(hold, X3, V3)
hold <- apply(hold, 1, function(x){
x[1:4] <- x[1:4] + tau_1^(x[5])*tau_0^(1 - x[5])
x})
hold <- t(hold)[, c("X1", "X2", "X3", "V1", "V2", "V3")]
X <- cbind(Int = 1, hold)
Z <- rbinom(n, 1, prob = plogis(X[, 1:4] %*% beta))
X <- cbind(X[, 1:4], Z, X[, 5:7])
data.frame(
Y = X %*% c(nu, xi) + rnorm(n),
X[ , -1])
}

To show that estimate_dr correctly computes $$\hat{\Delta}_{DR}$$, the results from geex can be compared to computing $$\hat{\Delta}_{DR}$$ “by hand” for a simulated dataset.

dt <- gen_data(1000, c(0, 0.6, -0.6, 0.6), c(0, -1, 1, -1, 2), c(-1, 1, 1))
geex_results <- estimate_dr(dt, Z ~ X1 + X2 + X3, Y ~ X1 + X2 + X3)
e  <- predict(glm(Z ~ X1 + X2 + X3, data = dt, family = "binomial"),
type = "response")
m0 <- predict(glm(Y ~ X1 + X2 + X3, data = dt, subset = Z==0), newdata = dt)
m1 <- predict(glm(Y ~ X1 + X2 + X3, data = dt, subset = Z==1), newdata = dt)
del_hat <- with(dt, mean( (Z * Y - (Z - e) * m1)/e)) -
with(dt, mean(((1 - Z) * Y - (Z - e) * m0)/(1 - e)))

Lunceford, Jared K., and Marie Davidian. 2004. Stratification and Weighting via the Propensity Score in Estimation of Causal Treatment Effects: A Comparative Study 23.

Perez-Heydrich, Carolina, Michael G. Hudgens, M. Elizabeth Halloran, John D. Clemens, Mohammad Ali, and Michael E. Emch. 2014. Assessing Effects of Cholera Vaccination in the Presence of Interference 70.