# tests/examples/set.targetE.examples.R In simcausal: Simulating Longitudinal Data with Causal Inference Applications

```#---------------------------------------------------------------------------------------
# EXAMPLE 1: DAG with single point treatment
#---------------------------------------------------------------------------------------
# Define a DAG with single-point treatment ("Anode")
D <- DAG.empty()
D <- D + node("W1", distr="rbern", prob=plogis(-0.5))
D <- D + node("W2", distr="rbern", prob=plogis(-0.5 + 0.5*W1))
D <- D + node("Anode", distr="rbern", prob=plogis(-0.5 - 0.3*W1 - 0.3*W2))
D <- D + node("Y", distr="rbern", prob=plogis(-0.1 + 1.2*Anode + 0.3*W1 + 0.3*W2),
EFU=TRUE)
D_WAY <- set.DAG(D)

# Defining interventions (actions)
# define action "A1" that sets the treatment node to constant 1
D_WAY <- D_WAY + action("A1", nodes=node("Anode",distr="rbern", prob=1))
# define another action "A0" that sets the treatment node to constant 0
D_WAY <- D_WAY + action("A0", nodes=node("Anode",distr="rbern", prob=0))
#---------------------------------------------------------------------------------------
# Defining and calculating causal parameters:
#---------------------------------------------------------------------------------------
# Counterfactual mean of node "Y" under action "A1"
D_WAY <- set.targetE(D_WAY, outcome="Y", param="A1")
eval.target(D_WAY, n=10000)

# Contrasts of means of "Y" under action "A1" minus action "A0"
D_WAY <- set.targetE(D_WAY, outcome="Y", param="A1-A0")
eval.target(D_WAY, n=10000)

# Ratios of "Y" under action "A1" over action "A0"
D_WAY <- set.targetE(D_WAY, outcome="Y", param="A1/A0")
eval.target(D_WAY, n=10000)

# Alternative parameter evaluation by passing already simulated full data to
# \code{eval.target}
X_dat1 <- simfull(A(D_WAY), n=10000)
D_WAY <- set.targetE(D_WAY, outcome="Y", param="A1/A0")
eval.target(D_WAY, data=X_dat1)

#---------------------------------------------------------------------------------------
# EXAMPLE 2: DAG with time-varying outcomes (survival outcome)
#---------------------------------------------------------------------------------------
# Define longitudinal data structure over 6 time-points t=(0:5)
t_end <- 5
D <- DAG.empty()
D <- D + node("L2", t=0, distr="rbern", prob=0.05)
D <- D + node("L1", t=0, distr="rbern", prob=ifelse(L2[0]==1,0.5,0.1))
D <- D + node("A1", t=0, distr="rbern", prob=ifelse(L1[0]==1 & L2[0]==0, 0.5,
ifelse(L1[0]==0 & L2[0]==0, 0.1,
ifelse(L1[0]==1 & L2[0]==1, 0.9, 0.5))))
D <- D + node("A2", t=0, distr="rbern", prob=0, order=4, EFU=TRUE)
D <- D + node("Y",  t=0, distr="rbern",
prob=plogis(-6.5 + L1[0] + 4*L2[0] + 0.05*I(L2[0]==0)),
EFU=TRUE)
D <- D + node("L2", t=1:t_end, distr="rbern", prob=ifelse(A1[t-1]==1, 0.1,
ifelse(L2[t-1]==1, 0.9,
min(1,0.1 + t/16))))
D <- D + node("A1", t=1:t_end, distr="rbern", prob=ifelse(A1[t-1]==1, 1,
ifelse(L1[0]==1 & L2[0]==0, 0.3,
ifelse(L1[0]==0 & L2[0]==0, 0.1,
ifelse(L1[0]==1 & L2[0]==1, 0.7,
0.5)))))
D <- D + node("A2", t=1:t_end, distr="rbern", prob=0, EFU=TRUE)
D <- D + node("Y",  t=1:t_end, distr="rbern",
prob=plogis(-6.5 + L1[0] + 4*L2[t] + 0.05*sum(I(L2[0:t]==rep(0,(t+1))))),
EFU=TRUE)
D <- set.DAG(D)

# Add two dynamic actions (indexed by values of the parameter theta={0,1})
# Define intervention nodes
act_t0_theta <- node("A1",t=0, distr="rbern", prob=ifelse(L2[0] >= theta,1,0))
act_tp_theta <- node("A1",t=1:t_end, distr="rbern",
prob=ifelse(A1[t-1]==1,1,ifelse(L2[t] >= theta,1,0)))
# Add two actions to current DAG object
D <- D + action("A1_th0", nodes=c(act_t0_theta, act_tp_theta), theta=0)
D <- D + action("A1_th1", nodes=c(act_t0_theta, act_tp_theta), theta=1)
#---------------------------------------------------------------------------------------
# Defining and calculating the target parameter
#---------------------------------------------------------------------------------------
# Counterfactual mean of node "Y" at time-point t=4 under action "A1_th0"
D <- set.targetE(D, outcome="Y", t=4, param="A1_th0")
eval.target(D, n=5000)

# Vector of counterfactual means of"Y" over all time points under action "A1_th1"
D <- set.targetE(D, outcome="Y", t=0:5, param="A1_th1")
eval.target(D, n=5000)

# Vector of counterfactual contrasts of "Y" over all time points
# for action "A1_th1" minus action "A1_th0"
D <- set.targetE(D, outcome="Y", t=0:5, param="A1_th1 - A1_th0")
eval.target(D, n=5000)

# Vector of counterfactual ratios of "Y" over all time points
# for action "A1_th0" over action "A1_th1"
D <- set.targetE(D, outcome="Y", t=0:5, param="A1_th0 / A1_th1")
eval.target(D, n=5000)
```

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simcausal documentation built on Oct. 9, 2017, 1:03 a.m.