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Updated variable selection vignette #58

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Jun 24, 2024
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Updated variable selection vignette #58

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30 changes: 9 additions & 21 deletions vignettes/CausalInference.Rmd
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Original file line number Diff line number Diff line change
Expand Up @@ -803,9 +803,9 @@ We draw from a modified "demo 1" DGP

```{r}
mu <- function(x) {1+g(x)+x[,1]*x[,3]-x[,2]+3*x[,3]}
tau <- function(x) {1+0.5*x[,1]}
tau <- function(x) {1 - 2*x[,1] + 2*x[,2] + 1*x[,1]*x[,2]}
n <- 500
snr <- 2
snr <- 4
x1 <- rnorm(n)
x2 <- rnorm(n)
x3 <- rnorm(n)
Expand All @@ -829,7 +829,7 @@ X$x4 <- factor(X$x4, ordered = TRUE)
X$x5 <- factor(X$x5, ordered = TRUE)

# Split data into test and train sets
test_set_pct <- 0.2
test_set_pct <- 0.5
n_test <- round(test_set_pct*n)
n_train <- n - n_test
test_inds <- sort(sample(1:n, n_test, replace = FALSE))
Expand Down Expand Up @@ -857,7 +857,7 @@ Here we simulate from the model with the original MCMC sampler, using all of the
```{r}
num_gfr <- 0
num_burnin <- 1000
num_mcmc <- 100
num_mcmc <- 1000
num_samples <- num_gfr + num_burnin + num_mcmc
bcf_model_mcmc <- bcf(
X_train = X_train, Z_train = Z_train, y_train = y_train, pi_train = pi_train,
Expand All @@ -879,9 +879,6 @@ abline(0,1,col="red",lty=3,lwd=3)
plot(rowMeans(bcf_model_mcmc$y_hat_test), y_test,
xlab = "predicted", ylab = "actual", main = "Outcome")
abline(0,1,col="red",lty=3,lwd=3)
plot(rowMeans(bcf_model_mcmc$y_hat_test-bcf_model_mcmc$mu_hat_test), tau_test*Z_test,
xlab = "predicted", ylab = "actual", main = "Treatment effect term")
abline(0,1,col="red",lty=3,lwd=3)
sigma_observed <- var(y-E_XZ)
plot_bounds <- c(min(c(bcf_model_mcmc$sigma2_samples, sigma_observed)),
max(c(bcf_model_mcmc$sigma2_samples, sigma_observed)))
Expand Down Expand Up @@ -918,14 +915,14 @@ Here we simulate from the model with the original MCMC sampler, using only covar
```{r}
num_gfr <- 0
num_burnin <- 1000
num_mcmc <- 100
num_mcmc <- 1000
num_samples <- num_gfr + num_burnin + num_mcmc
bcf_model_mcmc <- bcf(
X_train = X_train, Z_train = Z_train, y_train = y_train, pi_train = pi_train,
X_test = X_test, Z_test = Z_test, pi_test = pi_test,
num_gfr = num_gfr, num_burnin = num_burnin, num_mcmc = num_mcmc,
sample_sigma_leaf_mu = F, sample_sigma_leaf_tau = F,
keep_vars_tau = c("x1")
keep_vars_tau = c("x1","x2")
)
```

Expand All @@ -941,9 +938,6 @@ abline(0,1,col="red",lty=3,lwd=3)
plot(rowMeans(bcf_model_mcmc$y_hat_test), y_test,
xlab = "predicted", ylab = "actual", main = "Outcome")
abline(0,1,col="red",lty=3,lwd=3)
plot(rowMeans(bcf_model_mcmc$y_hat_test-bcf_model_mcmc$mu_hat_test), tau_test*Z_test,
xlab = "predicted", ylab = "actual", main = "Treatment effect term")
abline(0,1,col="red",lty=3,lwd=3)
sigma_observed <- var(y-E_XZ)
plot_bounds <- c(min(c(bcf_model_mcmc$sigma2_samples, sigma_observed)),
max(c(bcf_model_mcmc$sigma2_samples, sigma_observed)))
Expand Down Expand Up @@ -980,7 +974,7 @@ Here we simulate from the model with the warm-start sampler, using all of the co
```{r}
num_gfr <- 10
num_burnin <- 0
num_mcmc <- 100
num_mcmc <- 1000
num_samples <- num_gfr + num_burnin + num_mcmc
bcf_model_warmstart <- bcf(
X_train = X_train, Z_train = Z_train, y_train = y_train, pi_train = pi_train,
Expand All @@ -1002,9 +996,6 @@ abline(0,1,col="red",lty=3,lwd=3)
plot(rowMeans(bcf_model_warmstart$y_hat_test), y_test,
xlab = "predicted", ylab = "actual", main = "Outcome")
abline(0,1,col="red",lty=3,lwd=3)
plot(rowMeans(bcf_model_warmstart$y_hat_test - bcf_model_warmstart$mu_hat_test), tau_test*Z_test,
xlab = "predicted", ylab = "actual", main = "Treatment effect term")
abline(0,1,col="red",lty=3,lwd=3)
sigma_observed <- var(y-E_XZ)
plot_bounds <- c(min(c(bcf_model_warmstart$sigma2_samples, sigma_observed)),
max(c(bcf_model_warmstart$sigma2_samples, sigma_observed)))
Expand Down Expand Up @@ -1041,14 +1032,14 @@ Here we simulate from the model with the warm-start sampler, using only covariat
```{r}
num_gfr <- 10
num_burnin <- 0
num_mcmc <- 100
num_mcmc <- 1000
num_samples <- num_gfr + num_burnin + num_mcmc
bcf_model_warmstart <- bcf(
X_train = X_train, Z_train = Z_train, y_train = y_train, pi_train = pi_train,
X_test = X_test, Z_test = Z_test, pi_test = pi_test,
num_gfr = num_gfr, num_burnin = num_burnin, num_mcmc = num_mcmc,
sample_sigma_leaf_mu = F, sample_sigma_leaf_tau = F,
keep_vars_tau = c("x1"), random_seed = 2
keep_vars_tau = c("x1", "x2")
)
```

Expand All @@ -1064,9 +1055,6 @@ abline(0,1,col="red",lty=3,lwd=3)
plot(rowMeans(bcf_model_warmstart$y_hat_test), y_test,
xlab = "predicted", ylab = "actual", main = "Outcome")
abline(0,1,col="red",lty=3,lwd=3)
plot(rowMeans(bcf_model_warmstart$y_hat_test-bcf_model_warmstart$mu_hat_test), tau_test*Z_test,
xlab = "predicted", ylab = "actual", main = "Treatment effect term")
abline(0,1,col="red",lty=3,lwd=3)
sigma_observed <- var(y-E_XZ)
plot_bounds <- c(min(c(bcf_model_warmstart$sigma2_samples, sigma_observed)),
max(c(bcf_model_warmstart$sigma2_samples, sigma_observed)))
Expand Down
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