set.targetE: Define Non-Parametric Causal Parameters

In osofr/simcausal: Simulating Longitudinal Data with Causal Inference Applications

Define Non-Parametric Causal Parameters

Description

Set up the causal target parameter as a vector of expectations, ratio of expectations or contrast of expectations (average treatment effect) over the nodes of specified actions. These settings are then used to evaluate the true value of the causal target parameter by calling eval.target function.

Usage

set.targetE(DAG, outcome, t, param, ..., attr = list())

Arguments

DAG	Object specifying the directed acyclic graph (DAG) for the observed data
outcome	Name of the outcome node
t	Integer vector of time points to use for expectations, has to be omitted or NULL for non-time-varying DAGs.
param	A character vector "ActionName1", specifying the action name for the expectation target parameter; "ActionName1 / ActionName2", for the ratio of expectations of outcome nodes for actions "ActionName1" and "ActionName2"; "ActionName1 - ActionName2" for the contrast of expectations of outcome for actions "ActionName1" and "ActionName2"
...	Additional attributes (to be used in future versions)
attr	Additional attributes (to be used in future versions)

Value

A modified DAG object with the target parameter saved as part of the DAG, this DAG can now be passed as an argument to eval.target function for actual Monte-Carlo evaluation of the target parameter. See Examples.

References

Sofrygin O, van der Laan MJ, Neugebauer R (2017). "simcausal R Package: Conducting Transparent and Reproducible Simulation Studies of Causal Effect Estimation with Complex Longitudinal Data." Journal of Statistical Software, 81(2), 1-47. doi: 10.18637/jss.v081.i02.

Examples

#---------------------------------------------------------------------------------------
# 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)

osofr/simcausal documentation built on Oct. 21, 2022, 3:09 a.m.