diff --git a/DESCRIPTION b/DESCRIPTION
index f93b69e1e..1253c0cf8 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: tune
 Title: Tidy Tuning Tools
-Version: 1.1.1.9000
+Version: 1.1.1.9001
 Authors@R: c(
     person("Max", "Kuhn", , "max@posit.co", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-2402-136X")),
@@ -27,7 +27,7 @@ Imports:
     GPfit,
     hardhat (>= 1.2.0),
     lifecycle (>= 1.0.0),
-    parsnip (>= 1.0.2),
+    parsnip (>= 1.1.0.9001),
     purrr (>= 1.0.0),
     recipes (>= 1.0.4),
     rlang (>= 1.0.2),
@@ -37,7 +37,7 @@ Imports:
     tidyselect (>= 1.1.2),
     vctrs (>= 0.6.1),
     withr,
-    workflows (>= 1.0.0),
+    workflows (>= 1.1.3.9001),
     yardstick (>= 1.0.0)
 Suggests: 
     C50,
@@ -50,6 +50,9 @@ Suggests:
     testthat (>= 3.0.0),
     xgboost,
     xml2
+Remotes:
+    tidymodels/parsnip#955,
+    tidymodels/workflows#199
 Config/Needs/website: pkgdown, tidymodels, kknn, doParallel, doFuture,
     tidyverse/tidytemplate
 Config/testthat/edition: 3
diff --git a/NAMESPACE b/NAMESPACE
index 9540a53f7..f5196af79 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -127,6 +127,7 @@ S3method(vec_ptype2,tune_results.tune_results)
 S3method(vec_restore,iteration_results)
 S3method(vec_restore,resample_results)
 S3method(vec_restore,tune_results)
+S3method(weight_propensity,tune_results)
 export(.catch_and_log)
 export(.catch_and_log_fit)
 export(.config_key_from_metrics)
@@ -295,6 +296,7 @@ importFrom(hardhat,extract_workflow)
 importFrom(hardhat,tune)
 importFrom(parsnip,get_from_env)
 importFrom(parsnip,required_pkgs)
+importFrom(parsnip,weight_propensity)
 importFrom(purrr,map_int)
 importFrom(recipes,all_outcomes)
 importFrom(recipes,all_predictors)
diff --git a/R/weight_propensity.R b/R/weight_propensity.R
new file mode 100644
index 000000000..ddc53ec7c
--- /dev/null
+++ b/R/weight_propensity.R
@@ -0,0 +1,143 @@
+#' Helper for bridging two-stage causal fits
+#'
+#' @description
+#' `weight_propensity()` is a helper function to more easily link the
+#' propensity and outcome models in causal workflows. In the case of a
+#' single model fit, as with `model_fit`s or `workflow`s, the function
+#' is roughly analogous to an `augment()` method that additionally takes in
+#' a propensity weighting function. For `tune_results`, the method carries
+#' out this same augment-adjacent procedure on the training data underlying
+#' the resampling object for each element of the analysis set.
+#'
+#' @inheritParams parsnip::weight_propensity.model_fit
+#'
+#' @inherit parsnip::weight_propensity.model_fit return
+#'
+#' @inherit parsnip::weight_propensity.model_fit references
+#'
+#' @examplesIf tune:::should_run_examples(suggests = "modeldata")
+#' # load needed packages
+#' library(modeldata)
+#' library(parsnip)
+#' library(workflows)
+#' library(rsample)
+#'
+#' library(ggplot2)
+#' library(dplyr)
+#' library(purrr)
+#'
+#' # example data: model causal estimate for `Class`
+#' two_class_dat <- two_class_dat[1:250,]
+#' two_class_dat
+#'
+#' # see `propensity::wt_ate()` for a more realistic example
+#' # of a propensity weighting function
+#' silly_wt_fn <- function(.propensity, .exposure = NULL, ...) {
+#'   .propensity
+#' }
+#'
+#' propensity_wf <- workflow(Class ~ B, logistic_reg())
+#' outcome_wf <- workflow(A ~ Class, linear_reg()) %>% add_case_weights(.wts)
+#'
+#' # single model --------------------------------------------------------------
+#' propensity_fit <- fit(propensity_wf, two_class_dat)
+#'
+#' two_class_weighted <-
+#'   weight_propensity(propensity_fit, silly_wt_fn, data = two_class_dat)
+#'
+#' two_class_weighted
+#'
+#' outcome_fit <- fit(outcome_wf, two_class_weighted)
+#'
+#' outcome_fit %>% extract_fit_engine() %>% coef()
+#'
+#' # resampled model -----------------------------------------------------------
+#' set.seed(1)
+#' boots <- bootstraps(two_class_dat[1:250,], times = 100)
+#'
+#' res_tm <-
+#'   # fit the propensity model to resamples
+#'   fit_resamples(
+#'     propensity_wf,
+#'     resamples = boots,
+#'     # note `extract = identity` rather than `extract`
+#'     control = control_resamples(extract = identity)
+#'   ) %>%
+#'   # determine weights for outcome model based on
+#'   # propensity model's predictions
+#'   weight_propensity(silly_wt_fn) %>%
+#'   # fit outcome workflow using generated `.wts`
+#'   fit_resamples(
+#'     outcome_wf,
+#'     resamples = .,
+#'     # would usually `extract = tidy` here
+#'     control = control_resamples(extract = identity)
+#'   )
+#'
+#' # extracts contain the properly resampled fitted workflows:
+#' collect_extracts(res_tm)
+#'
+#' # plot the properly resampled distribution of estimates:
+#' collect_extracts(res_tm) %>%
+#'   pull(.extracts) %>%
+#'   map(extract_fit_engine) %>%
+#'   map(coef) %>%
+#'   bind_rows() %>%
+#'   ggplot() +
+#'   aes(x = ClassClass2) +
+#'   geom_histogram()
+#'
+#' @name weight_propensity
+#' @aliases weight_propensity.tune_results
+#' @importFrom parsnip weight_propensity
+#' @method weight_propensity tune_results
+#' @export
+weight_propensity.tune_results <- function(object, wt_fn, ...) {
+  if (rlang::is_missing(wt_fn) || !is.function(wt_fn)) {
+    cli::cli_abort("{.arg wt_fn} must be a function.")
+  }
+
+  wf_1 <- purrr::pluck(object, ".extracts", 1, ".extracts", 1)
+  if (!inherits(wf_1, "workflow")) {
+    cli::cli_abort(
+      "{.arg object} must have been generated with the \\
+       {.help [control option](tune::control_grid)} {.code extract = identity}."
+    )
+  }
+
+  dots <- rlang::list2(...)
+  if ("data" %in% names(dots)) {
+    cli::cli_abort(
+      "The {.cls tune_results} method for {.fn weight_propensity} does not take \\
+       a {.arg data} argument, but one was supplied."
+    )
+  }
+
+  for (resample in seq_along(object$splits)) {
+    object$splits[[resample]] <-
+      augment_split(
+        object$splits[[resample]],
+        object$.extracts[[resample]]$.extracts[[1]],
+        wt_fn = wt_fn,
+        ...
+      )
+  }
+
+  tibble::new_tibble(
+    object[, c("splits", "id")],
+    !!!attr(object, "rset_info")$att,
+    class = c(attr(object, "rset_info")$att$class, "rset")
+  )
+}
+
+augment_split <- function(split, workflow, wt_fn, ...) {
+  split[["data"]]$..id <- seq_along(split[["data"]][[1]])
+  d <- rsample::analysis(split)
+  d <- vctrs::vec_slice(d, !duplicated(d$..id))
+  d <- weight_propensity(workflow, wt_fn, ..., data = d)
+
+  split[["data"]][d$..id, ".wts"] <- d$.wts
+  split[["data"]]$..id <- NULL
+
+  split
+}
diff --git a/_pkgdown.yml b/_pkgdown.yml
index a0407dab9..e86978ce7 100644
--- a/_pkgdown.yml
+++ b/_pkgdown.yml
@@ -55,6 +55,7 @@ reference:
     - coord_obs_pred
     - conf_mat_resampled
     - example_ames_knn
+    - starts_with("weight_propensity")
   - title: Developer functions
     contents:
     - merge.recipe
diff --git a/man/weight_propensity.Rd b/man/weight_propensity.Rd
new file mode 100644
index 000000000..399c67a20
--- /dev/null
+++ b/man/weight_propensity.Rd
@@ -0,0 +1,119 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/weight_propensity.R
+\name{weight_propensity}
+\alias{weight_propensity}
+\alias{weight_propensity.tune_results}
+\title{Helper for bridging two-stage causal fits}
+\usage{
+\method{weight_propensity}{tune_results}(object, wt_fn, ...)
+}
+\arguments{
+\item{object}{The object containing the model fit(s) that will generate
+predictions used to calculate propensity weights. Currently, either a
+\link[parsnip:fit.model_spec]{parsnip model fit}, fitted
+\link[workflows:workflow]{workflow}, or
+tuning results (\code{?tune::fit_resamples}) object. If a tuning result, the
+object must have been generated with the control argument
+(\code{?tune::control_resamples}) \code{extract = identity}.}
+
+\item{wt_fn}{A function used to calculate the propensity weights. The first
+argument gives the predicted probability of exposure, the true value for
+which is provided in the second argument. See \code{?propensity::wt_ate()} for
+an example.}
+
+\item{...}{Additional arguments passed to \code{wt_fn}.}
+}
+\value{
+For \code{model_fit} and fitted \code{workflow} input, a modified version of the data
+set supplied in \code{data} that contains a \code{.wts} column with class
+\code{importance_weights}. For \code{tune_results} input, a modified version of the
+resampling object underlying the tuning results containing a new \code{.wts} column
+with propensity values corresponding to each element of the analysis set.
+}
+\description{
+\code{weight_propensity()} is a helper function to more easily link the
+propensity and outcome models in causal workflows. In the case of a
+single model fit, as with \code{model_fit}s or \code{workflow}s, the function
+is roughly analogous to an \code{augment()} method that additionally takes in
+a propensity weighting function. For \code{tune_results}, the method carries
+out this same augment-adjacent procedure on the training data underlying
+the resampling object for each element of the analysis set.
+}
+\examples{
+\dontshow{if (tune:::should_run_examples(suggests = "modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
+# load needed packages
+library(modeldata)
+library(parsnip)
+library(workflows)
+library(rsample)
+
+library(ggplot2)
+library(dplyr)
+library(purrr)
+
+# example data: model causal estimate for `Class`
+two_class_dat <- two_class_dat[1:250,]
+two_class_dat
+
+# see `propensity::wt_ate()` for a more realistic example
+# of a propensity weighting function
+silly_wt_fn <- function(.propensity, .exposure = NULL, ...) {
+  .propensity
+}
+
+propensity_wf <- workflow(Class ~ B, logistic_reg())
+outcome_wf <- workflow(A ~ Class, linear_reg()) \%>\% add_case_weights(.wts)
+
+# single model --------------------------------------------------------------
+propensity_fit <- fit(propensity_wf, two_class_dat)
+
+two_class_weighted <-
+  weight_propensity(propensity_fit, silly_wt_fn, data = two_class_dat)
+
+two_class_weighted
+
+outcome_fit <- fit(outcome_wf, two_class_weighted)
+
+outcome_fit \%>\% extract_fit_engine() \%>\% coef()
+
+# resampled model -----------------------------------------------------------
+set.seed(1)
+boots <- bootstraps(two_class_dat[1:250,], times = 100)
+
+res_tm <-
+  # fit the propensity model to resamples
+  fit_resamples(
+    propensity_wf,
+    resamples = boots,
+    # note `extract = identity` rather than `extract`
+    control = control_resamples(extract = identity)
+  ) \%>\%
+  # determine weights for outcome model based on
+  # propensity model's predictions
+  weight_propensity(silly_wt_fn) \%>\%
+  # fit outcome workflow using generated `.wts`
+  fit_resamples(
+    outcome_wf,
+    resamples = .,
+    # would usually `extract = tidy` here
+    control = control_resamples(extract = identity)
+  )
+
+# extracts contain the properly resampled fitted workflows:
+collect_extracts(res_tm)
+
+# plot the properly resampled distribution of estimates:
+collect_extracts(res_tm) \%>\%
+  pull(.extracts) \%>\%
+  map(extract_fit_engine) \%>\%
+  map(coef) \%>\%
+  bind_rows() \%>\%
+  ggplot() +
+  aes(x = ClassClass2) +
+  geom_histogram()
+\dontshow{\}) # examplesIf}
+}
+\references{
+Barrett M & D'Agostino McGowan L (forthcoming).
+\emph{Causal Inference in R}. \url{https://www.r-causal.org/}
+}
diff --git a/tests/testthat/_snaps/weight_propensity.md b/tests/testthat/_snaps/weight_propensity.md
new file mode 100644
index 000000000..e246aa85f
--- /dev/null
+++ b/tests/testthat/_snaps/weight_propensity.md
@@ -0,0 +1,42 @@
+# errors informatively with bad input
+
+    Code
+      weight_propensity(res_fit_resamples_bad, silly_wt_fn)
+    Condition
+      Error in `weight_propensity()`:
+      ! `object` must have been generated with the control option (`?tune::control_grid()`) `extract = identity`.
+
+---
+
+    Code
+      weight_propensity(res_fit_resamples)
+    Condition
+      Error in `weight_propensity()`:
+      ! `wt_fn` must be a function.
+
+---
+
+    Code
+      weight_propensity(res_fit_resamples, "boop")
+    Condition
+      Error in `weight_propensity()`:
+      ! `wt_fn` must be a function.
+
+---
+
+    Code
+      weight_propensity(res_fit_resamples, function(...) {
+        -1L
+      })
+    Condition
+      Error in `hardhat::importance_weights()`:
+      ! `x` can't contain negative weights.
+
+---
+
+    Code
+      weight_propensity(res_fit_resamples, silly_wt_fn, data = two_class_dat)
+    Condition
+      Error in `weight_propensity()`:
+      ! The <tune_results> method for `weight_propensity()` does not take a `data` argument, but one was supplied.
+
diff --git a/tests/testthat/test-weight_propensity.R b/tests/testthat/test-weight_propensity.R
new file mode 100644
index 000000000..a14358716
--- /dev/null
+++ b/tests/testthat/test-weight_propensity.R
@@ -0,0 +1,186 @@
+test_that("basic functionality", {
+  skip_on_cran()
+  skip_if_not_installed("modeldata")
+  library(modeldata)
+  library(parsnip)
+  library(workflows)
+  library(rsample)
+
+  silly_wt_fn <- function(.propensity, .exposure = NULL, ...) {
+    .propensity
+  }
+
+  propensity_wf <- workflow(Class ~ B, logistic_reg())
+  outcome_wf <- workflow(A ~ Class, linear_reg()) %>% add_case_weights(.wts)
+
+  set.seed(1)
+  boots <- bootstraps(two_class_dat)
+
+  res_fit_resamples <-
+    fit_resamples(
+      propensity_wf,
+      resamples = boots,
+      # can't be `save_workflow = TRUE`, as we need the _fitted_ workflow
+      control = control_resamples(extract = identity)
+    )
+
+  res_weight_propensity <-
+    res_fit_resamples %>%
+    weight_propensity(silly_wt_fn)
+
+  # `weight_propensity()` preserves rset properties:
+  preserved <- c("class", "times", "apparent", "breaks", "pool")
+  expect_equal(attributes(boots)[preserved], attributes(res_weight_propensity)[preserved])
+  expect_equal(boots$id, res_weight_propensity$id)
+  expect_equal(
+    purrr::map(boots$splits, purrr::pluck, "in_id"),
+    purrr::map(res_weight_propensity$splits, purrr::pluck, "in_id")
+  )
+  expect_equal(
+    purrr::map(boots$splits, purrr::pluck, "data"),
+    purrr::map(res_weight_propensity$splits, purrr::pluck, "data") %>%
+      purrr::map(dplyr::select, -.wts)
+  )
+
+  # confirming that `.wts` change with every resample. this expectation
+  # specifically only works because `wt_fn ~= identity`
+  wf_fits <- purrr::map(res_fit_resamples$.extracts, purrr::pluck, ".extracts", 1)
+  expect_equal(
+    purrr::map(boots$splits, analysis) %>%
+      purrr::map2(wf_fits, ., predict, type = "prob") %>%
+      purrr::map(dplyr::pull, 2),
+    purrr::map(res_weight_propensity$splits, analysis) %>%
+      purrr::map(dplyr::pull, .wts) %>%
+      purrr::map(as.numeric)
+  )
+
+  # output is valid input to another call to `fit_resamples()`
+  res_final <-
+    res_weight_propensity %>%
+    fit_resamples(outcome_wf, resamples = .)
+
+  expect_s3_class(res_final, "tune_results")
+})
+
+test_that("errors informatively with bad input", {
+  skip_if_not_installed("modeldata")
+  library(modeldata)
+  library(parsnip)
+  library(workflows)
+  library(rsample)
+
+  silly_wt_fn <- function(.propensity, .exposure = NULL, ...) {
+    .propensity
+  }
+
+
+  propensity_wf <- workflow(Class ~ B, logistic_reg())
+  outcome_wf <- workflow(A ~ Class, linear_reg()) %>% add_case_weights(.wts)
+
+  set.seed(1)
+  boots <- bootstraps(two_class_dat)
+
+  res_fit_resamples <-
+    fit_resamples(
+      propensity_wf,
+      resamples = boots,
+      # can't be `save_workflow = TRUE`, as we need the _fitted_ workflow
+      control = control_resamples(extract = identity)
+    )
+
+  res_fit_resamples_bad <-
+    fit_resamples(propensity_wf, resamples = boots)
+
+  res_weight_propensity <-
+    res_fit_resamples %>%
+    weight_propensity(silly_wt_fn)
+
+  # did not set `control`
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(res_fit_resamples_bad, silly_wt_fn)
+  )
+
+  # bad `wt_fn`
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(res_fit_resamples)
+  )
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(res_fit_resamples, "boop")
+  )
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(res_fit_resamples, function(...) {-1L})
+  )
+
+  # mistakenly supplied `data`
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(res_fit_resamples, silly_wt_fn, data = two_class_dat)
+  )
+})
+
+test_that("results match manual calculation", {
+  skip_on_cran()
+  skip_if_not_installed("modeldata")
+  library(modeldata)
+  library(parsnip)
+  library(workflows)
+  library(rsample)
+  library(hardhat)
+
+  silly_wt_fn <- function(.propensity, .exposure = NULL, ...) {
+    .propensity
+  }
+
+  propensity_wf <- workflow(Class ~ B, logistic_reg())
+  outcome_wf <- workflow(A ~ Class, linear_reg()) %>% add_case_weights(.wts)
+
+  set.seed(1)
+  boots <- bootstraps(two_class_dat)
+
+  # our way:
+  res_tm <-
+    fit_resamples(
+      propensity_wf,
+      resamples = boots,
+      control = control_resamples(extract = identity)
+    ) %>%
+    weight_propensity(silly_wt_fn) %>%
+    fit_resamples(
+      outcome_wf,
+      resamples = .,
+      # would usually `extract = tidy` here, but don't want to
+      # register broom:::tidy.lm
+      control = control_resamples(extract = identity)
+    )
+
+  # a la r-causal:
+  fit_ipw <- function(split, ...) {
+    .df <- analysis(split)
+
+    propensity_model <- fit(propensity_wf, .df)
+
+    preds <- predict(propensity_model, new_data = .df, type = "prob")
+    .df$.wts <- importance_weights(silly_wt_fn(preds[[2]]))
+
+    fit(outcome_wf, .df)
+  }
+
+  res_rc <- purrr::map(boots$splits, fit_ipw)
+
+  # comparison:
+  expect_equal(
+    res_tm %>%
+      collect_extracts() %>%
+      pull(.extracts) %>%
+      purrr::map(extract_fit_engine) %>%
+      purrr::map(coef),
+    purrr::map(res_rc, extract_fit_engine) %>%
+      purrr::map(coef)
+  )
+})