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Select Covariate Adjustment Sets from Data

Source: R/dagitty.R
select_controls.Rd

Wraps adjustmentSets to construct a dataset with covariates that (asymptotically) allow unbiased estimation of causal effects from observational data.

Usage

select_controls(
  x,
  data,
  exposure = NULL,
  outcome = NULL,
  which_set = c("first", "sample", "all"),
  ...
)

Arguments

x

An input graph of class dagitty.

data

A data.frame or object coercible by as.data.frame().

exposure

Atomic character, name of the exposure variable.

outcome

Atomic character, name of the outcome variable.

which_set

Atomic character, indicating which set of covariates to select in case there are multiple. Valid choices are in c("first", "sample", "all"), see Value.

...

Other arguments passed to adjustmentSets

Value

If which_set = "all", returns a list of data.frames to allow for sensitivity analyses. Otherwise, returns a data.frame.

See also

adjustmentSets

Examples

dag <- dagitty::dagitty('dag {x -> y}')
df <- data.frame(x = rnorm(10), y = rnorm(10))
df1 <- select_controls(dag, df, exposure = "x", outcome = "y")
class(df1) == "data.frame"
#> [1] TRUE
df2 <- select_controls(dag, df, exposure = "x", outcome = "y", which_set = "sample")
class(df2) == "data.frame"
#> [1] TRUE
lst1 <- select_controls(dag, df, exposure = "x", outcome = "y", which_set = "all")
class(lst1) == "list"
#> [1] TRUE