Structureal Causal Model

gistfile1.txt

=100000

p_g = 0.1
p_s1 = 0.2
p_s2 = 0.4
p_l1 = 0.05
p_l2 = 0.20
p_l3 = 0.30
U_G  = rbinom(n,1,p_g)
U_S1 = rbinom(n,1,p_s1)
U_S2 = rbinom(n,1,p_s2)
U_L1 = rbinom(n,1,p_l1)
U_L2 = rbinom(n,1,p_l2)
U_L3 = rbinom(n,1,p_l3)

G = U_G
S = U_S1 * (1-G) + U_S2 * G
L = U_L1 * (1-G)*(1-S) + U_L2*((1-G)*S + (1-S)*G)  +  U_L3 * G * S

L0 = U_L1*(1-G) + U_L2*G
L1 = U_L2*(1-G) + U_L3*G

df <- data.frame(G, S, L)

cod1 = sum(df[df$S==1,]$L)/sum(df$S)
cod0 =  sum(df[df$S==0,]$L)/(n-sum(df$S))

print("causal effect of smoking")
print((sum(L1) - sum(L0))/n)
print("difference of contional probabilities")
print(cod1-cod0)