updated stan code

Author: joonho112

.DS_Store

@@ -0,0 +1,59 @@
+data {
+  int<lower=1> N;               // Sample size N 
+  int<lower=0, upper=1> Z[N];   // Treatment assigned Z
+  int<lower=0, upper=1> W[N];   // Treatment received W  
+  int<lower=0, upper=1> Y[N];   // Outcome Y  
+}
+
+parameters {
+  // Population probability of being a complier
+  real<lower=0, upper=1> pi_c;
+  
+  // Probabilities for the binomial outcome distributions
+  real<lower=0, upper=1> eta_c0;
+  real<lower=0, upper=1> eta_c1;
+  real<lower=0, upper=1> eta_n;
+}  
+
+transformed parameters {
+  // Superpopulation complier average causal effect (CACE)
+  // in per-1000 units
+  real CACE = (eta_c1 - eta_c0) * 10^3;
+}
+
+model {
+  // Define local variables for efficiency
+  real log_pi_c = log(pi_c);
+  real log1m_pi_c = log1m(pi_c);
+  
+  // Prior for Complier probability
+  // implicit prior: pi_c ~ Unif(0, 1)
+  
+  // Priors for outcome model parameters
+  eta_c0 ~ beta(2, 2);  
+  eta_c1 ~ beta(2, 2);  
+  eta_n ~ beta(2, 2); 
+
+  // Likelihood
+  for(n in 1:N){
+    
+    // Complier (assigned to treatment)
+    if (Z[n] == 1 && W[n] == 1){
+      target += log_pi_c + bernoulli_lpmf(Y[n] | eta_c1) ;
+    }
+    
+    // Never-taker (assigned to treatment)
+    else if (Z[n] == 1 && W[n] == 0){
+      target +=  log1m_pi_c + bernoulli_lpmf(Y[n] | eta_n);
+    }
+    
+    // Complier or Never-taker (assigned to control)
+    else if (Z[n] == 0 && W[n] == 0){
+      target += log_mix(
+        pi_c,                           // Complier probability
+        bernoulli_lpmf(Y[n] | eta_c0),  // Complier
+        bernoulli_lpmf(Y[n] | eta_n)    // Never-taker
+      );  
+    }
+  }
+}

education/.DS_Store

@@ -0,0 +1,61 @@
+data {
+  int<lower=1> N;               // Sample size N 
+  int<lower=0, upper=1> Z[N];   // Treatment assigned Z
+  int<lower=0, upper=1> W[N];   // Treatment received W  
+  int<lower=0, upper=1> Y[N];   // Outcome Y  
+}
+
+parameters {
+  // Population probability of being a complier
+  real<lower=0, upper=1> pi_c;
+  
+  // Probabilities for the binomial outcome distributions
+  real<lower=0, upper=1> eta_c0;
+  real<lower=0, upper=1> eta_c1;
+  real<lower=0, upper=1> eta_n0;
+  real<lower=0, upper=1> eta_n1;
+} 
+
+transformed parameters {
+  // Super-population average causal effects 
+  real CACE = (eta_c1 - eta_c0) * 10^3;
+  real NACE = (eta_n1 - eta_n0) * 10^3;
+}
+
+model {
+  // Define local variables for efficiency
+  real log_pi_c = log(pi_c);
+  real log1m_pi_c = log1m(pi_c);
+  
+  // Prior for Complier probability
+  // implicit prior: pi_c ~ Unif(0, 1)
+  
+  // Priors for outcome model parameters
+  eta_c0 ~ beta(2, 2);  
+  eta_c1 ~ beta(2, 2);  
+  eta_n0 ~ beta(2, 2);
+  eta_n1 ~ beta(2, 2);
+
+  // Likelihood
+  for(n in 1:N){
+    
+    // Complier (assigned to treatment)
+    if (Z[n] == 1 && W[n] == 1){
+      target += log_pi_c + bernoulli_lpmf(Y[n] | eta_c1) ;
+    }
+    
+    // Never-taker (assigned to treatment)
+    else if (Z[n] == 1 && W[n] == 0){
+      target +=  log1m_pi_c + bernoulli_lpmf(Y[n] | eta_n1);
+    }
+    
+    // Complier or Never-taker (assigned to control)
+    else if (Z[n] == 0 && W[n] == 0){
+      target += log_mix(
+        pi_c,                            // Complier probability
+        bernoulli_lpmf(Y[n] | eta_c0),   // Complier
+        bernoulli_lpmf(Y[n] | eta_n0)    // Never-taker
+      );
+    }
+  }
+}