jointIda: Estimate Multiset of Possible Total Joint Effects

Description

jointIda() estimates the multiset of possible total joint effects of a set of intervention variables (X) on another variable (Y) from observational data. This is a version of ida that allows multiple simultaneous interventions.

Usage

jointIda(x.pos, y.pos, mcov, graphEst = NULL, all.pasets = NULL,
         technique = c("RRC", "MCD"), type = c("pdag", "cpdag", "dag"))

Arguments

x.pos

(integer vector) positions of the intervention variables X in the covariance matrix.

y.pos

(integer) position of variable Y in the covariance matrix. (y.pos can also be an integer vector, see Note.)

mcov

(estimated) covariance matrix.

graphEst

(graphNEL object) Estimated CPDAG or PDAG. The CPDAG is typically from pc(): If the result of pc is pc.fit, the estimated CPDAG can be obtained by pc.fit@graph. The PDAG can be obtained from CPDAG by adding bacground knowledge using addBgKnowledge(). graphEst can only be considered if all.pasets is NULL.

all.pasets

(an optional argument and the default is NULL) A list where each element is a list of size length(x.pos). Each sub-list all.pasets[[i]] contains possible parent sets of x.pos in the same order, i.e., all.pasets[[i]][[j]] is a possible parent set of x.pos[j]. This option can be used if possible parent sets of the intervention variables are known.

technique

character string specifying the technique that will be used to estimate the total joint causal effects (given the parent sets), see details below.

"RRC":: Recursive regressions for causal effects.
"MCD":: Modifying the Cholesky decomposition.

type

Type of graph "graphEst"; can be of type "cpdag", "pdag" (e.g. a CPDAG with background knowledge from Meek, 1995) or "dag".

Value

A matrix representing the multiset containing the estimated possible total joint effects of X on Y. The number of rows is equal to length(x.pos), i.e., each column represents a vector of possible joint causal effects.

Details

It is assumed that we have observational data that are multivariate Gaussian and faithful to the true (but unknown) underlying causal DAG (without hidden variables). Under these assumptions, this function estimates the multiset of possible total joint effects of X on Y. Here the total joint effect of \(X = (X_1,X_2)\) on \(Y\) is defined via Pearl's do-calculus as the vector \((E[Y|do(X_1=x_1+1,X_2=x_2)]-E[Y|do(X_1=x_1,X_2=x_2)], E[Y|do(X_1=x_1,X_2=x_2+1)]-E[Y|do(X_1=x_1,X_2=x_2)])\), with a similar definition for more than two variables. These values are equal to the partial derivatives (evaluated at \((x_1,x_2)\)) of \(E[Y|do(X=x_1',X_2=x_2')]\) with respect to \(x_1'\) and \(x_2'\). Moreover, under the Gaussian assumption, these partial derivatives do not depend on the values at which they are evaluated.

We estimate a multiset of possible total joint effects instead of the unique total joint effect, since it is typically impossible to identify the latter when the true underlying causal DAG is unknown (even with an infinite amount of data).

Conceptually, the method works as follows. First, we estimate the CPDAG or PDAG based on the data. The CPDAG represents the equivalence class of DAGs and can be estimated from observational data with the function pc (see the help file of this function).

The PDAG contains more orientations than the CPDAG and thus, represents a smaller equivalence class of DAGs, compared to the CPDAG. We can obtain a PDAG if we have background knowledge of, for example, certain edge orientations of undirected edges in the CPDAG. We obtain the PDAG by adding these orientations to the CPDAG using the function addBgKnowledge (see the help file of this function).

Then using the CPDAG or PDAG we extract a collection of "jointly valid" parent sets of the intervention variables from the estimated CPDAG. For each set of jointly valid parent sets we apply RRC (recursive regressions for causal effects) or MCD (modifying the Cholesky decomposition) to estimate the total joint effect of X on Y from the sample covariance matrix (see Section 3 of Nandy et. al, 2015).

References

P. Nandy, M.H. Maathuis and T.S. Richardson (2017). Estimating the effect of joint interventions from observational data in sparse high-dimensional settings. In Annals of Statistics.

E. Perkovic, M. Kalisch and M.H. Maathuis (2017). Interpreting and using CPDAGs with background knowledge. In Proceedings of UAI 2017.

Examples

Run this code

# NOT RUN {
## Create a weighted DAG
p <- 6
V <- as.character(1:p)
edL <- list(
  "1" = list(edges=c(3,4), weights=c(1.1,0.3)),
  "2" = list(edges=c(6),  weights=c(0.4)),
  "3" = list(edges=c(2,4,6),weights=c(0.6,0.8,0.9)),
  "4" = list(edges=c(2),weights=c(0.5)),
  "5" = list(edges=c(1,4),weights=c(0.2,0.7)),
  "6" = NULL)
myDAG <- new("graphNEL", nodes=V, edgeL=edL, edgemode="directed") ## true DAG
myCPDAG <- dag2cpdag(myDAG) ## true CPDAG
myPDAG <- addBgKnowledge(myCPDAG,1,3) ## true PDAG with background knowledge 1 -> 3
covTrue <- trueCov(myDAG) ## true covariance matrix

n <- 1000
## simulate Gaussian data from the true DAG
dat <- if (require("mvtnorm")) {
  set.seed(123)
  rmvnorm(n, mean=rep(0,p), sigma=covTrue)
} else readRDS(system.file(package="pcalg", "external", "N_6_1000.rds"))

## estimate CPDAG and PDAG -- see  help(pc), help(addBgKnoweldge)
suffStat <- list(C = cor(dat), n = n)
pc.fit <- pc(suffStat, indepTest = gaussCItest, p = p, alpha = 0.01, u2pd="relaxed")
pc.fit.pdag <- addBgKnowledge(pc.fit@graph,1,3)

if (require(Rgraphviz)) {
  ## plot the true and estimated graphs
  par(mfrow = c(1,3))
  plot(myDAG, main = "True DAG")
  plot(pc.fit, main = "Estimated CPDAG")
  plot(pc.fit.pdag, main = "Estimated PDAG")
}

## Suppose that we know the true CPDAG and covariance matrix
jointIda(x.pos=c(1,2), y.pos=6, covTrue, graphEst=myCPDAG, technique="RRC", type = "cpdag")
jointIda(x.pos=c(1,2), y.pos=6, covTrue, graphEst=myCPDAG, technique="MCD", type = "cpdag")

## Suppose that we know the true PDAG and covariance matrix
jointIda(x.pos=c(1,2), y.pos=6, covTrue, graphEst=myPDAG, technique="RRC", type = "pdag")
jointIda(x.pos=c(1,2), y.pos=6, covTrue, graphEst=myPDAG, technique="MCD", type = "pdag")

## Instead of knowing the true CPDAG or PDAG, it is enough to know only
## the jointly valid parent sets of the intervention variables
## to use RRC or MCD
## all.jointly.valid.pasets:
ajv.pasets <- list(list(5,c(3,4)),list(integer(0),c(3,4)),list(3,c(3,4)))
jointIda(x.pos=c(1,2), y.pos=6, covTrue, all.pasets=ajv.pasets, technique="RRC")
jointIda(x.pos=c(1,2), y.pos=6, covTrue, all.pasets=ajv.pasets, technique="MCD")

## From the true DAG, we can compute the true total joint effects
## using RRC or MCD
cat("Dim covTrue: ", dim(covTrue),"\n")
jointIda(x.pos=c(1,2), y.pos=6, covTrue, graphEst=myDAG, technique="RRC", type = "dag")
jointIda(x.pos=c(1,2), y.pos=6, covTrue, graphEst=myDAG, technique="MCD", type = "dag")


## When working with data, we have to use the estimated CPDAG or PDAG
## and the sample covariance matrix
jointIda(x.pos=c(1,2), y.pos=6, cov(dat), graphEst=pc.fit@graph, technique="RRC", type = "cpdag")
jointIda(x.pos=c(1,2), y.pos=6, cov(dat), graphEst=pc.fit@graph, technique="MCD", type = "cpdag")

jointIda(x.pos=c(1,2), y.pos=6, cov(dat), graphEst=pc.fit.pdag, technique="RRC", type = "pdag")
jointIda(x.pos=c(1,2), y.pos=6, cov(dat), graphEst=pc.fit.pdag, technique="MCD", type = "pdag")
## RRC and MCD can produce different results when working with data

## jointIda also works when x.pos has length 1 and in the following example
## it gives the same result as ida() (see Note)
##
## When the CPDAG is known
jointIda(x.pos=1, y.pos=6, covTrue, graphEst=myCPDAG, technique="RRC", type = "cpdag")
ida(x.pos=1, y.pos=6, covTrue, graphEst=myCPDAG, method="global", type = "cpdag")

## When the PDAG is known
jointIda(x.pos=1, y.pos=6, covTrue, graphEst=myPDAG, technique="RRC", type = "pdag")
ida(x.pos=1, y.pos=6, covTrue, graphEst=myPDAG, method="global", type = "pdag")

## When the DAG is known
jointIda(x.pos=1, y.pos=6, covTrue, graphEst=myDAG, technique="RRC", type = "dag")
ida(x.pos=1, y.pos=6, covTrue, graphEst=myDAG, method="global")
## Note that, causalEffect(myDAG,y=6,x=1) does not give the correct value in this case,
## since this function requires that the variables are in a causal order.

# }

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