diff --git a/doc/api.rst b/doc/api.rst
index 1966b48a37..42f9fec299 100644
--- a/doc/api.rst
+++ b/doc/api.rst
@@ -171,6 +171,7 @@ spikeinterface.preprocessing
     .. autofunction:: interpolate_bad_channels
     .. autofunction:: normalize_by_quantile
     .. autofunction:: notch_filter
+    .. autofunction:: causal_filter
     .. autofunction:: phase_shift
     .. autofunction:: rectify
     .. autofunction:: remove_artifacts
diff --git a/src/spikeinterface/preprocessing/filter.py b/src/spikeinterface/preprocessing/filter.py
index 54c5ab2b2d..a67d163d3d 100644
--- a/src/spikeinterface/preprocessing/filter.py
+++ b/src/spikeinterface/preprocessing/filter.py
@@ -24,10 +24,12 @@
 
 class FilterRecording(BasePreprocessor):
     """
-    Generic filter class based on:
-
-      * scipy.signal.iirfilter
-      * scipy.signal.filtfilt or scipy.signal.sosfilt
+    A generic filter class based on:
+        For filter coefficient generation:
+            * scipy.signal.iirfilter
+        For filter application:
+            * scipy.signal.filtfilt or scipy.signal.sosfiltfilt when direction = "forward-backward"
+            * scipy.signal.lfilter or scipy.signal.sosfilt when direction = "forward" or "backward"
 
     BandpassFilterRecording is built on top of it.
 
@@ -56,6 +58,11 @@ class FilterRecording(BasePreprocessor):
         - numerator/denominator : ("ba")
     ftype : str, default: "butter"
         Filter type for `scipy.signal.iirfilter` e.g. "butter", "cheby1".
+    direction : "forward" | "backward" | "forward-backward", default: "forward-backward"
+        Direction of filtering:
+        - "forward" - filter is applied to the timeseries in one direction, creating phase shifts
+        - "backward" - the timeseries is reversed, the filter is applied and filtered timeseries reversed again. Creates phase shifts in the opposite direction to "forward"
+        - "forward-backward" - Applies the filter in the forward and backward direction, resulting in zero-phase filtering. Note this doubles the effective filter order.
 
     Returns
     -------
@@ -75,6 +82,7 @@ def __init__(
         add_reflect_padding=False,
         coeff=None,
         dtype=None,
+        direction="forward-backward",
     ):
         import scipy.signal
 
@@ -106,7 +114,13 @@ def __init__(
         for parent_segment in recording._recording_segments:
             self.add_recording_segment(
                 FilterRecordingSegment(
-                    parent_segment, filter_coeff, filter_mode, margin, dtype, add_reflect_padding=add_reflect_padding
+                    parent_segment,
+                    filter_coeff,
+                    filter_mode,
+                    margin,
+                    dtype,
+                    add_reflect_padding=add_reflect_padding,
+                    direction=direction,
                 )
             )
 
@@ -121,14 +135,25 @@ def __init__(
             margin_ms=margin_ms,
             add_reflect_padding=add_reflect_padding,
             dtype=dtype.str,
+            direction=direction,
         )
 
 
 class FilterRecordingSegment(BasePreprocessorSegment):
-    def __init__(self, parent_recording_segment, coeff, filter_mode, margin, dtype, add_reflect_padding=False):
+    def __init__(
+        self,
+        parent_recording_segment,
+        coeff,
+        filter_mode,
+        margin,
+        dtype,
+        add_reflect_padding=False,
+        direction="forward-backward",
+    ):
         BasePreprocessorSegment.__init__(self, parent_recording_segment)
         self.coeff = coeff
         self.filter_mode = filter_mode
+        self.direction = direction
         self.margin = margin
         self.add_reflect_padding = add_reflect_padding
         self.dtype = dtype
@@ -150,11 +175,24 @@ def get_traces(self, start_frame, end_frame, channel_indices):
 
         import scipy.signal
 
-        if self.filter_mode == "sos":
-            filtered_traces = scipy.signal.sosfiltfilt(self.coeff, traces_chunk, axis=0)
-        elif self.filter_mode == "ba":
-            b, a = self.coeff
-            filtered_traces = scipy.signal.filtfilt(b, a, traces_chunk, axis=0)
+        if self.direction == "forward-backward":
+            if self.filter_mode == "sos":
+                filtered_traces = scipy.signal.sosfiltfilt(self.coeff, traces_chunk, axis=0)
+            elif self.filter_mode == "ba":
+                b, a = self.coeff
+                filtered_traces = scipy.signal.filtfilt(b, a, traces_chunk, axis=0)
+        else:
+            if self.direction == "backward":
+                traces_chunk = np.flip(traces_chunk, axis=0)
+
+            if self.filter_mode == "sos":
+                filtered_traces = scipy.signal.sosfilt(self.coeff, traces_chunk, axis=0)
+            elif self.filter_mode == "ba":
+                b, a = self.coeff
+                filtered_traces = scipy.signal.lfilter(b, a, traces_chunk, axis=0)
+
+            if self.direction == "backward":
+                filtered_traces = np.flip(filtered_traces, axis=0)
 
         if right_margin > 0:
             filtered_traces = filtered_traces[left_margin:-right_margin, :]
@@ -289,6 +327,73 @@ def __init__(self, recording, freq=3000, q=30, margin_ms=5.0, dtype=None):
 notch_filter = define_function_from_class(source_class=NotchFilterRecording, name="notch_filter")
 highpass_filter = define_function_from_class(source_class=HighpassFilterRecording, name="highpass_filter")
 
+
+def causal_filter(
+    recording,
+    direction="forward",
+    band=[300.0, 6000.0],
+    btype="bandpass",
+    filter_order=5,
+    ftype="butter",
+    filter_mode="sos",
+    margin_ms=5.0,
+    add_reflect_padding=False,
+    coeff=None,
+    dtype=None,
+):
+    """
+    Generic causal filter built on top of the filter function.
+
+    Parameters
+    ----------
+    recording : Recording
+        The recording extractor to be re-referenced
+    direction : "forward" | "backward", default: "forward"
+        Direction of causal filter. The "backward" option flips the traces in time before applying the filter
+        and then flips them back.
+    band : float or list, default: [300.0, 6000.0]
+        If float, cutoff frequency in Hz for "highpass" filter type
+        If list. band (low, high) in Hz for "bandpass" filter type
+    btype : "bandpass" | "highpass", default: "bandpass"
+        Type of the filter
+    margin_ms : float, default: 5.0
+        Margin in ms on border to avoid border effect
+    coeff : array | None, default: None
+        Filter coefficients in the filter_mode form.
+    dtype : dtype or None, default: None
+        The dtype of the returned traces. If None, the dtype of the parent recording is used
+    add_reflect_padding : Bool, default False
+        If True, uses a left and right margin during calculation.
+    filter_order : order
+        The order of the filter for `scipy.signal.iirfilter`
+    filter_mode :  "sos" | "ba", default: "sos"
+        Filter form of the filter coefficients for `scipy.signal.iirfilter`:
+        - second-order sections ("sos")
+        - numerator/denominator : ("ba")
+    ftype : str, default: "butter"
+        Filter type for `scipy.signal.iirfilter` e.g. "butter", "cheby1".
+
+    Returns
+    -------
+    filter_recording : FilterRecording
+        The causal-filtered recording extractor object
+    """
+    assert direction in ["forward", "backward"], "Direction must be either 'forward' or 'backward'"
+    return filter(
+        recording=recording,
+        direction=direction,
+        band=band,
+        btype=btype,
+        filter_order=filter_order,
+        ftype=ftype,
+        filter_mode=filter_mode,
+        margin_ms=margin_ms,
+        add_reflect_padding=add_reflect_padding,
+        coeff=coeff,
+        dtype=dtype,
+    )
+
+
 bandpass_filter.__doc__ = bandpass_filter.__doc__.format(_common_filter_docs)
 highpass_filter.__doc__ = highpass_filter.__doc__.format(_common_filter_docs)
 
diff --git a/src/spikeinterface/preprocessing/preprocessinglist.py b/src/spikeinterface/preprocessing/preprocessinglist.py
index 149c6eb458..bdf5f2219c 100644
--- a/src/spikeinterface/preprocessing/preprocessinglist.py
+++ b/src/spikeinterface/preprocessing/preprocessinglist.py
@@ -12,6 +12,7 @@
     notch_filter,
     HighpassFilterRecording,
     highpass_filter,
+    causal_filter,
 )
 from .filter_gaussian import GaussianFilterRecording, gaussian_filter
 from .normalize_scale import (
diff --git a/src/spikeinterface/preprocessing/tests/test_filter.py b/src/spikeinterface/preprocessing/tests/test_filter.py
index 68790b3273..9df60af3db 100644
--- a/src/spikeinterface/preprocessing/tests/test_filter.py
+++ b/src/spikeinterface/preprocessing/tests/test_filter.py
@@ -4,7 +4,140 @@
 from spikeinterface.core import generate_recording
 from spikeinterface import NumpyRecording, set_global_tmp_folder
 
-from spikeinterface.preprocessing import filter, bandpass_filter, notch_filter
+from spikeinterface.preprocessing import filter, bandpass_filter, notch_filter, causal_filter
+
+
+class TestCausalFilter:
+    """
+    The only thing that is not tested (JZ, as of 23/07/2024) is the
+    propagation of margin kwargs, these are general filter params
+    and can be tested in an upcoming PR.
+    """
+
+    @pytest.fixture(scope="session")
+    def recording_and_data(self):
+        recording = generate_recording(durations=[1])
+        raw_data = recording.get_traces()
+
+        return (recording, raw_data)
+
+    def test_causal_filter_main_kwargs(self, recording_and_data):
+        """
+        Perform a test that expected output is returned under change
+        of all key filter-related kwargs. First run the filter in
+        the forward direction with options and compare it
+        to the expected output from scipy.
+
+        Next, change every filter-related kwarg and set in the backwards
+        direction. Again check it matches expected scipy output.
+        """
+        from scipy.signal import lfilter, sosfilt
+
+        recording, raw_data = recording_and_data
+
+        # First, check in the forward direction with
+        # the default set of kwargs
+        options = self._get_filter_options()
+
+        sos = self._run_iirfilter(options, recording)
+
+        test_data = sosfilt(sos, raw_data, axis=0)
+        test_data.astype(recording.dtype)
+
+        filt_data = causal_filter(recording, direction="forward", **options, margin_ms=0).get_traces()
+
+        assert np.allclose(test_data, filt_data, rtol=0, atol=1e-6)
+
+        # Then, change all kwargs to ensure they are propagated
+        # and check the backwards version.
+        options["band"] = [671]
+        options["btype"] = "highpass"
+        options["filter_order"] = 8
+        options["ftype"] = "bessel"
+        options["filter_mode"] = "ba"
+        options["dtype"] = np.float16
+
+        b, a = self._run_iirfilter(options, recording)
+
+        flip_raw = np.flip(raw_data, axis=0)
+        test_data = lfilter(b, a, flip_raw, axis=0)
+        test_data = np.flip(test_data, axis=0)
+        test_data = test_data.astype(options["dtype"])
+
+        filt_data = causal_filter(recording, direction="backward", **options, margin_ms=0).get_traces()
+
+        assert np.allclose(test_data, filt_data, rtol=0, atol=1e-6)
+
+    def test_causal_filter_custom_coeff(self, recording_and_data):
+        """
+        A different path is taken when custom coeff is selected.
+        Therefore, explicitly test the expected outputs are obtained
+        when passing custom coeff, under the "ba" and "sos" conditions.
+        """
+        from scipy.signal import lfilter, sosfilt
+
+        recording, raw_data = recording_and_data
+
+        options = self._get_filter_options()
+        options["filter_mode"] = "ba"
+        options["coeff"] = (np.array([0.1, 0.2, 0.3]), np.array([0.4, 0.5, 0.6]))
+
+        # Check the custom coeff are propagated in both modes.
+        # First, in "ba" mode
+        test_data = lfilter(options["coeff"][0], options["coeff"][1], raw_data, axis=0)
+        test_data = test_data.astype(recording.get_dtype())
+
+        filt_data = causal_filter(recording, direction="forward", **options, margin_ms=0).get_traces()
+
+        assert np.allclose(test_data, filt_data, rtol=0, atol=1e-6, equal_nan=True)
+
+        # Next, in "sos" mode
+        options["filter_mode"] = "sos"
+        options["coeff"] = np.ones((2, 6))
+
+        test_data = sosfilt(options["coeff"], raw_data, axis=0)
+        test_data = test_data.astype(recording.get_dtype())
+
+        filt_data = causal_filter(recording, direction="forward", **options, margin_ms=0).get_traces()
+
+        assert np.allclose(test_data, filt_data, rtol=0, atol=1e-6, equal_nan=True)
+
+    def test_causal_kwarg_error_raised(self, recording_and_data):
+        """
+        Test that passing the "forward-backward" direction results in
+        an error. It is is critical this error is raised,
+        otherwise the filter will no longer be causal.
+        """
+        recording, raw_data = recording_and_data
+
+        with pytest.raises(BaseException) as e:
+            filt_data = causal_filter(recording, direction="forward-backward")
+
+    def _run_iirfilter(self, options, recording):
+        """
+        Convenience function to convert Si kwarg
+        names to Scipy.
+        """
+        from scipy.signal import iirfilter
+
+        return iirfilter(
+            N=options["filter_order"],
+            Wn=options["band"],
+            btype=options["btype"],
+            ftype=options["ftype"],
+            output=options["filter_mode"],
+            fs=recording.get_sampling_frequency(),
+        )
+
+    def _get_filter_options(self):
+        return {
+            "band": [300.0, 6000.0],
+            "btype": "bandpass",
+            "filter_order": 5,
+            "ftype": "butter",
+            "filter_mode": "sos",
+            "coeff": None,
+        }
 
 
 def test_filter():
@@ -28,6 +161,8 @@ def test_filter():
     # other filtering types
     rec3 = filter(rec, band=500.0, btype="highpass", filter_mode="ba", filter_order=2)
     rec4 = notch_filter(rec, freq=3000, q=30, margin_ms=5.0)
+    rec5 = causal_filter(rec, direction="forward")
+    rec6 = causal_filter(rec, direction="backward")
 
     # filter from coefficients
     from scipy.signal import iirfilter