diff --git a/external_confs/dm/acs.yaml b/external_confs/dm/acs.yaml
new file mode 100644
index 00000000..1c013720
--- /dev/null
+++ b/external_confs/dm/acs.yaml
@@ -0,0 +1,4 @@
+stratified_sampler: approx_group
+num_workers: 1
+batch_size_tr: 128
+batch_size_te: 100000
diff --git a/external_confs/ds/acs/employment_dis_fl.yaml b/external_confs/ds/acs/employment_dis_fl.yaml
new file mode 100644
index 00000000..3315fe45
--- /dev/null
+++ b/external_confs/ds/acs/employment_dis_fl.yaml
@@ -0,0 +1,10 @@
+---
+defaults:
+  - acs
+
+setting: employment_disability
+survey_year: YEAR_2018
+states:
+  - FL
+survey: PERSON
+horizon: ONE_YEAR
diff --git a/external_confs/experiment/acs/fcn.yaml b/external_confs/experiment/acs/fcn.yaml
new file mode 100644
index 00000000..5c84bc9d
--- /dev/null
+++ b/external_confs/experiment/acs/fcn.yaml
@@ -0,0 +1,38 @@
+# @package _global_
+
+defaults:
+    - /alg: supmatch_no_disc
+    - override /dm: acs
+    - override /ds: acs/employment_dis_fl
+    - override /split: acs/employment_dis
+    - override /labeller: gt
+    - override /ae_arch: fcn
+
+alg:
+    use_amp: False
+    pred:
+        lr: ${ ae.lr }
+    steps: 10000
+    val_freq: 1000
+    log_freq: ${ alg.steps }
+    # num_disc_updates: 3
+    # disc_loss_w: 0.03
+    # ga_steps: 1
+    # max_grad_norm: null
+
+ae:
+    recon_loss: l2
+    zs_dim: 1
+
+ae_opt:
+    lr: 1.e-4
+    optimizer_cls: ADAM
+    weight_decay: 0
+
+ae_arch:
+    hidden_dim: 64
+    latent_dim: 64
+    num_hidden: 2
+
+eval:
+    batch_size: 128
diff --git a/external_confs/split/acs/employment_dis.yaml b/external_confs/split/acs/employment_dis.yaml
new file mode 100644
index 00000000..ee8cdae9
--- /dev/null
+++ b/external_confs/split/acs/employment_dis.yaml
@@ -0,0 +1,8 @@
+---
+defaults:
+  - tabular
+
+seed: 0
+train_props:
+  1:
+    0: 0.0
diff --git a/poetry.lock b/poetry.lock
index 998ea5a7..6d1ca546 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -543,6 +543,7 @@ files = [
 
 [package.dependencies]
 filelock = "*"
+folktables = {version = ">=0.0.12", optional = true, markers = "extra == \"data\" or extra == \"all\""}
 jinja2 = "*"
 joblib = ">=1.1.0,<2.0.0"
 networkx = "*"
@@ -578,6 +579,23 @@ files = [
 docs = ["furo (>=2023.3.27)", "sphinx (>=6.1.3)", "sphinx-autodoc-typehints (>=1.23,!=1.23.4)"]
 testing = ["covdefaults (>=2.3)", "coverage (>=7.2.3)", "diff-cover (>=7.5)", "pytest (>=7.3.1)", "pytest-cov (>=4)", "pytest-mock (>=3.10)", "pytest-timeout (>=2.1)"]
 
+[[package]]
+name = "folktables"
+version = "0.0.12"
+description = "New machine learning benchmarks from tabular datasets."
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "folktables-0.0.12-py3-none-any.whl", hash = "sha256:979cda1900094b845ab3a8a3ae1b848f0138b780d5f8d17eeb6eb04c3c0c6617"},
+    {file = "folktables-0.0.12.tar.gz", hash = "sha256:e83dde0cbcdd54c7c39b175006a50bdfc4adc351f69d4389f82aaba3eee02115"},
+]
+
+[package.dependencies]
+numpy = "*"
+pandas = "*"
+requests = "*"
+scikit-learn = "*"
+
 [[package]]
 name = "fonttools"
 version = "4.39.4"
@@ -2855,18 +2873,20 @@ opt-einsum = ["opt-einsum (>=3.3)"]
 
 [[package]]
 name = "torch-conduit"
-version = "0.4.1"
+version = "0.4.2"
 description = "Lightweight framework for dataloading with PyTorch and channeling the power of PyTorch Lightning"
 optional = false
 python-versions = ">=3.10,<3.13"
 files = [
-    {file = "torch_conduit-0.4.1-py3-none-any.whl", hash = "sha256:d9e61232a7a017fd1ed7a9a0e080fb321b10ea816608518523b9c8ae9c9db44a"},
-    {file = "torch_conduit-0.4.1.tar.gz", hash = "sha256:4201f46355f7397c6e0bc1b39442b83daf207a9427ed93eebf0c1cbf59f67bd2"},
+    {file = "torch_conduit-0.4.2-py3-none-any.whl", hash = "sha256:ff8587f60d4fc79e298e0d883364a33710c1861ba8de2a5b4a6b17ddc3c46054"},
+    {file = "torch_conduit-0.4.2.tar.gz", hash = "sha256:9e4b3091f4f276b829a51a81639fa27ecaf7373958adb0f8306f3f01e36fc076"},
 ]
 
 [package.dependencies]
 albumentations = {version = ">=1.0.0,<2.0.0", optional = true, markers = "extra == \"image\" or extra == \"all\""}
 attrs = ">=21.2.0"
+ethicml = {version = ">=1.2.1,<2.0.0", extras = ["data"], optional = true, markers = "extra == \"fair\" or extra == \"all\""}
+folktables = {version = ">=0.0.12,<0.0.13", optional = true, markers = "extra == \"fair\" or extra == \"all\""}
 numpy = ">=1.22.3,<2.0.0"
 opencv-python = {version = ">=4.5.3,<5.0.0", optional = true, markers = "extra == \"image\" or extra == \"all\""}
 pandas = ">=1.3.3,<3.0"
@@ -3011,13 +3031,13 @@ files = [
 
 [[package]]
 name = "typing-extensions"
-version = "4.5.0"
-description = "Backported and Experimental Type Hints for Python 3.7+"
+version = "4.10.0"
+description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.5.0-py3-none-any.whl", hash = "sha256:fb33085c39dd998ac16d1431ebc293a8b3eedd00fd4a32de0ff79002c19511b4"},
-    {file = "typing_extensions-4.5.0.tar.gz", hash = "sha256:5cb5f4a79139d699607b3ef622a1dedafa84e115ab0024e0d9c044a9479ca7cb"},
+    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
+    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
 ]
 
 [[package]]
@@ -3238,4 +3258,4 @@ multidict = ">=4.0"
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.10,<3.13"
-content-hash = "0db240d14519c4a8a2078b91e59ce42803c85dbceb5d91b719ba7f6836c875f5"
+content-hash = "8e3dc2a8b7a8b97ff2ee17fec3b445c7ec645340c40c5f39b7a1cf552b54e5f4"
diff --git a/pyproject.toml b/pyproject.toml
index 0a01cebe..4bf6849e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -30,7 +30,8 @@ scikit-image = ">=0.14"
 scikit_learn = { version = ">=0.20.1" }
 scipy = { version = ">=1.2.1" }
 seaborn = { version = ">=0.9.0" }
-torch-conduit = { version = ">=0.3.4", extras = ["image"] }
+torch-conduit = { version = ">=0.4.2", extras = ["image", "fair"] }
+typing_extensions = ">= 4.10"
 
 tqdm = { version = ">=4.31.1" }
 typer = "*"
diff --git a/src/data/__init__.py b/src/data/__init__.py
index f2124c5f..5ad2129a 100644
--- a/src/data/__init__.py
+++ b/src/data/__init__.py
@@ -1,6 +1,6 @@
 from .common import *
 from .data_module import *
-from .nico_plus_plus import *
+from .factories import *
 from .nih import *
 from .splitter import *
 from .utils import *
diff --git a/src/data/factories.py b/src/data/factories.py
new file mode 100644
index 00000000..170bd1ff
--- /dev/null
+++ b/src/data/factories.py
@@ -0,0 +1,50 @@
+"""Dataset factories."""
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Optional, Union
+from typing_extensions import override
+
+from conduit.data.datasets.vision import NICOPP, NicoPPTarget
+from conduit.fair.data.datasets import (
+    ACSDataset,
+    ACSHorizon,
+    ACSSetting,
+    ACSState,
+    ACSSurvey,
+    ACSSurveyYear,
+)
+
+from src.data.common import DatasetFactory
+
+__all__ = ["NICOPPCfg"]
+
+
+@dataclass
+class NICOPPCfg(DatasetFactory):
+    root: Union[Path, str]
+    target_attrs: Optional[list[NicoPPTarget]] = None
+    transform: Any = None  # Optional[Union[Compose, BasicTransform, Callable[[Image], Any]]]
+
+    @override
+    def __call__(self) -> NICOPP:
+        return NICOPP(root=self.root, transform=self.transform, superclasses=self.target_attrs)
+
+
+@dataclass
+class ACSCfg(DatasetFactory):
+    setting: ACSSetting
+    survey_year: ACSSurveyYear = ACSSurveyYear.YEAR_2018
+    horizon: ACSHorizon = ACSHorizon.ONE_YEAR
+    survey: ACSSurvey = ACSSurvey.PERSON
+    states: list[ACSState] = field(default_factory=lambda: [ACSState.AL])
+
+    @override
+    def __call__(self) -> ACSDataset:
+        return ACSDataset(
+            setting=self.setting,
+            survey_year=self.survey_year,
+            horizon=self.horizon,
+            survey=self.survey,
+            states=self.states,
+        )
diff --git a/src/data/nico_plus_plus.py b/src/data/nico_plus_plus.py
deleted file mode 100644
index d3673f46..00000000
--- a/src/data/nico_plus_plus.py
+++ /dev/null
@@ -1,24 +0,0 @@
-"""NICO Dataset."""
-from dataclasses import dataclass
-from pathlib import Path
-from typing import Any, Optional, Union
-from typing_extensions import override
-
-from conduit.data.datasets.vision import CdtVisionDataset, NICOPP, NicoPPTarget
-from conduit.data.structures import TernarySample
-from torch import Tensor
-
-from src.data.common import DatasetFactory
-
-__all__ = ["NICOPPCfg"]
-
-
-@dataclass
-class NICOPPCfg(DatasetFactory):
-    root: Union[Path, str]
-    target_attrs: Optional[list[NicoPPTarget]] = None
-    transform: Any = None  # Optional[Union[Compose, BasicTransform, Callable[[Image], Any]]]
-
-    @override
-    def __call__(self) -> CdtVisionDataset[TernarySample, Tensor, Tensor]:
-        return NICOPP(root=self.root, transform=self.transform, superclasses=self.target_attrs)
diff --git a/src/data/splitter.py b/src/data/splitter.py
index 48c22a18..e9634a0b 100644
--- a/src/data/splitter.py
+++ b/src/data/splitter.py
@@ -1,5 +1,7 @@
-from abc import abstractmethod
+from abc import ABC, abstractmethod
+from collections.abc import Callable
 from dataclasses import dataclass
+from enum import Enum
 from pathlib import Path
 import platform
 from tempfile import TemporaryDirectory
@@ -10,7 +12,10 @@
 from conduit.data.datasets import random_split
 from conduit.data.datasets.utils import stratified_split
 from conduit.data.datasets.vision import CdtVisionDataset, ImageTform, PillowTform
+from conduit.fair.data.datasets import ACSDataset
+from conduit.transforms import MinMaxNormalize, TabularNormalize, ZScoreNormalize
 from loguru import logger
+from ranzen import some
 import torch
 from torch import Tensor
 import torchvision.transforms as T
@@ -24,14 +29,22 @@
     "DataSplitter",
     "RandomSplitter",
     "SplitFromArtifact",
+    "TabularSplitter",
     "load_split_inds_from_artifact",
     "save_split_inds_as_artifact",
 ]
 
 
 @dataclass(eq=False)
-class DataSplitter:
-    """How to split the data into train/test/dep."""
+class DataSplitter(ABC):
+    @abstractmethod
+    def __call__(self, dataset: D) -> TrainDepTestSplit[D]:
+        """Split the dataset into train/deployment/test."""
+
+
+@dataclass(eq=False)
+class _VisionDataSplitter(DataSplitter):
+    """Common methods for transforming vision datasets."""
 
     transductive: bool = False
     """Whether to include the test data in the pool of unlabelled data."""
@@ -133,7 +146,7 @@ def save_split_inds_as_artifact(
 
 
 @dataclass(eq=False)
-class RandomSplitter(DataSplitter):
+class RandomSplitter(_VisionDataSplitter):
     seed: int = 42
     dep_prop: float = 0.4
     test_prop: float = 0.2
@@ -259,7 +272,7 @@ def load_split_inds_from_artifact(
 
 
 @dataclass(eq=False, kw_only=True)
-class SplitFromArtifact(DataSplitter):
+class SplitFromArtifact(_VisionDataSplitter):
     artifact_name: str
     version: Optional[int] = None
 
@@ -272,3 +285,41 @@ def split(self, dataset: D) -> TrainDepTestSplit[D]:
         dep_data = dataset.subset(splits["dep"])
         test_data = dataset.subset(splits["test"])
         return TrainDepTestSplit(train=train_data, deployment=dep_data, test=test_data)
+
+
+class TabularTform(Enum):
+    zscore_normalize = (ZScoreNormalize,)
+    minmax_normalize = (MinMaxNormalize,)
+
+    def __init__(self, tform: Callable[[], TabularNormalize]) -> None:
+        self.tf = tform
+
+
+@dataclass(eq=False)
+class TabularSplitter(DataSplitter):
+    """Split and transform tabular datasets."""
+
+    seed: int
+    train_props: dict[int, dict[int, float]] | None = None
+    dep_prop: float = 0.2
+    test_prop: float = 0.1
+    transform: TabularTform | None = TabularTform.zscore_normalize
+
+    @override
+    def __call__(self, dataset: D) -> TrainDepTestSplit[D]:
+        if not isinstance(dataset, ACSDataset):
+            raise NotImplementedError("TabularSplitter only supports splitting of `ACSDataset`.")
+
+        train, dep, test = dataset.subsampled_split(
+            train_props=self.train_props,
+            val_prop=self.dep_prop,
+            test_prop=self.test_prop,
+            seed=self.seed,
+        )
+        if some(tf_type := self.transform):
+            tf = tf_type.tf()
+            train.fit_transform_(tf)
+            dep.transform_(tf)
+            test.transform_(tf)
+
+        return TrainDepTestSplit(train=train, deployment=dep, test=test)
diff --git a/src/relay/base.py b/src/relay/base.py
index 4f9ccbe8..bb8bafb2 100644
--- a/src/relay/base.py
+++ b/src/relay/base.py
@@ -8,7 +8,7 @@
 
 from src.data import DataModule, DataModuleConf, RandomSplitter, SplitFromArtifact
 from src.data.common import Dataset
-from src.data.splitter import DataSplitter
+from src.data.splitter import DataSplitter, TabularSplitter
 from src.labelling import Labeller
 from src.logging import WandbConf
 
@@ -23,7 +23,11 @@ class BaseRelay:
     seed: int = 0
 
     options: ClassVar[dict[str, dict[str, type]]] = {
-        "split": {"random": RandomSplitter, "artifact": SplitFromArtifact}
+        "split": {
+            "random": RandomSplitter,
+            "artifact": SplitFromArtifact,
+            "tabular": TabularSplitter,
+        }
     }
 
     def init_dm(
diff --git a/src/relay/fs.py b/src/relay/fs.py
index 2c41a6a2..ebbbb127 100644
--- a/src/relay/fs.py
+++ b/src/relay/fs.py
@@ -8,6 +8,7 @@
 from src.arch.backbones import DenseNet, LinearResNet, ResNet, SimpleCNN
 from src.arch.predictors.fcn import Fcn
 from src.data import DatasetFactory, NICOPPCfg, NIHChestXRayDatasetCfg
+from src.data.factories import ACSCfg
 from src.hydra_confs.datasets import Camelyon17Cfg, CelebACfg, ColoredMNISTCfg
 from src.labelling.pipeline import (
     CentroidalLabelNoiser,
@@ -44,6 +45,7 @@ class FsRelay(BaseRelay):
 
     options: ClassVar[dict[str, dict[str, type]]] = BaseRelay.options | {
         "ds": {
+            "acs": ACSCfg,
             "cmnist": ColoredMNISTCfg,
             "celeba": CelebACfg,
             "camelyon17": Camelyon17Cfg,
diff --git a/src/relay/label.py b/src/relay/label.py
index 0fed1397..8e745997 100644
--- a/src/relay/label.py
+++ b/src/relay/label.py
@@ -3,6 +3,7 @@
 from attrs import define, field
 
 from src.data.common import DatasetFactory
+from src.data.factories import ACSCfg
 from src.data.nih import NIHChestXRayDatasetCfg
 from src.data.utils import resolve_device
 from src.hydra_confs.datasets import Camelyon17Cfg, CelebACfg, ColoredMNISTCfg
@@ -31,6 +32,7 @@ class LabelRelay(BaseRelay):
 
     options: ClassVar[dict[str, dict[str, type]]] = BaseRelay.options | {
         "ds": {
+            "acs": ACSCfg,
             "cmnist": ColoredMNISTCfg,
             "celeba": CelebACfg,
             "camelyon17": Camelyon17Cfg,
diff --git a/src/relay/split.py b/src/relay/split.py
index d73363e7..7c888110 100644
--- a/src/relay/split.py
+++ b/src/relay/split.py
@@ -4,7 +4,7 @@
 
 from src.data import RandomSplitter
 from src.data.common import DatasetFactory
-from src.data.nico_plus_plus import NICOPPCfg
+from src.data.factories import NICOPPCfg
 from src.data.nih import NIHChestXRayDatasetCfg
 from src.hydra_confs.datasets import Camelyon17Cfg, CelebACfg
 from src.logging import WandbConf
diff --git a/src/relay/supmatch.py b/src/relay/supmatch.py
index e06e6b05..c5b4dc68 100644
--- a/src/relay/supmatch.py
+++ b/src/relay/supmatch.py
@@ -21,7 +21,7 @@
 from src.arch.predictors.base import PredictorFactory
 from src.arch.predictors.fcn import Fcn, SetFcn
 from src.data.common import DatasetFactory
-from src.data.nico_plus_plus import NICOPPCfg
+from src.data.factories import ACSCfg, NICOPPCfg
 from src.data.nih import NIHChestXRayDatasetCfg
 from src.hydra_confs.datasets import Camelyon17Cfg, CelebACfg, ColoredMNISTCfg
 from src.labelling.pipeline import (
@@ -71,6 +71,7 @@ class SupMatchRelay(BaseRelay):
     options: ClassVar[dict[str, dict[str, type]]] = BaseRelay.options | {
         "scorer": {"neural": NeuralScorer, "none": NullScorer},
         "ds": {
+            "acs": ACSCfg,
             "cmnist": ColoredMNISTCfg,
             "celeba": CelebACfg,
             "camelyon17": Camelyon17Cfg,