diff --git a/DESCRIPTION b/DESCRIPTION
index 4da4cdbd..ba74167d 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: workflows
 Title: Modeling Workflows
-Version: 1.1.3.9000
+Version: 1.1.3.9001
 Authors@R: c(
     person("Davis", "Vaughan", , "davis@posit.co", role = "aut"),
     person("Simon", "Couch", , "simon.couch@posit.co", role = c("aut", "cre"),
@@ -24,7 +24,7 @@ Imports:
     hardhat (>= 1.2.0),
     lifecycle (>= 1.0.3),
     modelenv (>= 0.1.0),
-    parsnip (>= 1.0.3),
+    parsnip (>= 1.1.0.9001),
     rlang (>= 1.0.3),
     tidyselect (>= 1.2.0),
     vctrs (>= 0.4.1)
@@ -38,6 +38,8 @@ Suggests:
     recipes (>= 1.0.0),
     rmarkdown,
     testthat (>= 3.0.0)
+Remotes:
+    tidymodels/parsnip#955
 VignetteBuilder: 
     knitr
 Config/Needs/website:
diff --git a/NAMESPACE b/NAMESPACE
index f1bac48f..5b8d8d5f 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -17,6 +17,7 @@ S3method(print,workflow)
 S3method(tidy,workflow)
 S3method(tunable,workflow)
 S3method(tune_args,workflow)
+S3method(weight_propensity,workflow)
 export(.fit_finalize)
 export(.fit_model)
 export(.fit_pre)
@@ -69,4 +70,5 @@ importFrom(hardhat,extract_recipe)
 importFrom(hardhat,extract_spec_parsnip)
 importFrom(lifecycle,deprecated)
 importFrom(parsnip,fit_xy)
+importFrom(parsnip,weight_propensity)
 importFrom(stats,predict)
diff --git a/R/weight_propensity.R b/R/weight_propensity.R
new file mode 100644
index 00000000..8e3d11d0
--- /dev/null
+++ b/R/weight_propensity.R
@@ -0,0 +1,43 @@
+#' Helper for bridging two-stage causal fits
+#'
+#' @inherit parsnip::weight_propensity.model_fit description
+#'
+#' @inheritParams parsnip::weight_propensity.model_fit
+#'
+#' @inherit parsnip::weight_propensity.model_fit return
+#'
+#' @inherit parsnip::weight_propensity.model_fit references
+#'
+#' @importFrom parsnip weight_propensity
+#' @method weight_propensity workflow
+#' @export
+weight_propensity.workflow <- function(object,
+                                       wt_fn,
+                                       .treated = extract_fit_parsnip(object)$lvl[2],
+                                       ...,
+                                       data) {
+  if (rlang::is_missing(wt_fn) || !is.function(wt_fn)) {
+    abort("`wt_fn` must be a function.")
+  }
+
+  if (rlang::is_missing(data) || !is.data.frame(data)) {
+    abort("`data` must be the data supplied as the data argument to `fit()`.")
+  }
+
+  if (!is_trained_workflow(object)) {
+    abort("`weight_propensity()` is not well-defined for an unfitted workflow.")
+  }
+
+  outcome_name <- names(object$pre$mold$outcomes)
+
+  preds <- predict(object, data, type = "prob")
+  preds <- preds[[paste0(".pred_", .treated)]]
+
+  data$.wts <-
+    hardhat::importance_weights(
+      wt_fn(preds, data[[outcome_name]], .treated = .treated, ...)
+    )
+
+  data
+}
+
diff --git a/man/weight_propensity.workflow.Rd b/man/weight_propensity.workflow.Rd
new file mode 100644
index 00000000..f4c0db27
--- /dev/null
+++ b/man/weight_propensity.workflow.Rd
@@ -0,0 +1,53 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/weight_propensity.R
+\name{weight_propensity.workflow}
+\alias{weight_propensity.workflow}
+\title{Helper for bridging two-stage causal fits}
+\usage{
+\method{weight_propensity}{workflow}(
+  object,
+  wt_fn,
+  .treated = extract_fit_parsnip(object)$lvl[2],
+  ...,
+  data
+)
+}
+\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{.treated}{The level of the exposure corresponding to the treatment, as
+a string. Additionally passed as \code{.treated} to \code{wt_fn}.}
+
+\item{...}{Additional arguments passed to \code{wt_fn}.}
+
+\item{data}{The data supplied as the \code{data} argument to \code{fit()} the \code{object}.
+This argument is only required for the \code{model_fit} and \code{workflow} methods---the
+needed data for the \code{tune_results} method lives inside of \code{object}.}
+}
+\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. \strong{The main documentation
+for this function lives in the tune package at} \code{?tune::weight_propensity}.
+}
+\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 00000000..9f18d07f
--- /dev/null
+++ b/tests/testthat/_snaps/weight_propensity.md
@@ -0,0 +1,50 @@
+# errors informatively with bad input
+
+    Code
+      weight_propensity(wf, silly_wt_fn, data = two_class_dat)
+    Condition
+      Error in `weight_propensity()`:
+      ! `weight_propensity()` is not well-defined for an unfitted workflow.
+
+---
+
+    Code
+      weight_propensity(wf_fit, data = two_class_dat)
+    Condition
+      Error in `weight_propensity()`:
+      ! `wt_fn` must be a function.
+
+---
+
+    Code
+      weight_propensity(wf_fit, "boop", data = two_class_dat)
+    Condition
+      Error in `weight_propensity()`:
+      ! `wt_fn` must be a function.
+
+---
+
+    Code
+      weight_propensity(wf_fit, function(...) {
+        -1L
+      }, data = two_class_dat)
+    Condition
+      Error in `hardhat::importance_weights()`:
+      ! `x` can't contain negative weights.
+
+---
+
+    Code
+      weight_propensity(wf_fit, silly_wt_fn)
+    Condition
+      Error in `weight_propensity()`:
+      ! `data` must be the data supplied as the data argument to `fit()`.
+
+---
+
+    Code
+      weight_propensity(wf_fit, silly_wt_fn, data = "boop")
+    Condition
+      Error in `weight_propensity()`:
+      ! `data` must be the data supplied as the data argument to `fit()`.
+
diff --git a/tests/testthat/test-weight_propensity.R b/tests/testthat/test-weight_propensity.R
new file mode 100644
index 00000000..0f3cedfa
--- /dev/null
+++ b/tests/testthat/test-weight_propensity.R
@@ -0,0 +1,63 @@
+test_that("basic functionality", {
+  skip_if_not_installed("modeldata")
+  library(modeldata)
+  library(parsnip)
+
+  silly_wt_fn <- function(.propensity, .exposure = NULL, ...) {
+    seq(1, 2, length.out = length(.propensity))
+  }
+
+  lr_fit <- fit(workflow(Class ~ A + B, logistic_reg()), two_class_dat)
+
+  lr_res1 <- weight_propensity(lr_fit, silly_wt_fn, data = two_class_dat)
+  expect_s3_class(lr_res1, "tbl_df")
+  expect_true(all(names(lr_res1) %in% c(names(two_class_dat), ".wts")))
+  expect_equal(lr_res1$.wts, importance_weights(seq(1, 2, length.out = nrow(two_class_dat))))
+})
+
+test_that("errors informatively with bad input", {
+  skip_if_not_installed("modeldata")
+  library(modeldata)
+  library(parsnip)
+
+  silly_wt_fn <- function(.propensity, .exposure = NULL, ...) {
+    seq(1, 2, length.out = length(.propensity))
+  }
+
+  # untrained workflow
+  wf <- workflow(Class ~ A + B, logistic_reg())
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(wf, silly_wt_fn, data = two_class_dat)
+  )
+
+  # bad `wt_fn`
+  wf_fit <- fit(wf, two_class_dat)
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(wf_fit, data = two_class_dat)
+  )
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(wf_fit, "boop", data = two_class_dat)
+  )
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(wf_fit, function(...) {-1L}, data = two_class_dat)
+  )
+
+  # bad `data`
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(wf_fit, silly_wt_fn)
+  )
+
+  expect_snapshot(
+    error = TRUE,
+    weight_propensity(wf_fit, silly_wt_fn, data = "boop")
+  )
+})