diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 4fc01dc..dac0982 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -34,6 +34,13 @@ jobs:
           python -m tiny_anomaly_lab --output ci_output
           test -f ci_output/report.html
           test -f ci_output/model_card.md
+          test -f ci_output/scores.csv
+          test -f ci_output/flagged_rows.csv
+      - name: Run the Mahalanobis detector demo
+        run: |
+          python -m tiny_anomaly_lab --detector mahalanobis --output ci_output_mah
+          test -f ci_output_mah/report.html
+          test -f ci_output_mah/flagged_rows.csv
 
       - name: Upload artifacts
         if: always()
diff --git a/README.md b/README.md
index 9be9af4..8fd0383 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@ An explainable anomaly-detection workbench for small time-series datasets.
 
 It runs one leakage-safe experiment on bundled data and writes a reproducible
 interactive report. The lab shows feature pipelines, evaluation without leakage,
-model cards and interactive visualization.
+model cards, per-feature contributions and interactive visualization.
 
 ## Scope
 
@@ -28,8 +28,9 @@ secret or network call is needed for the demo or for the tests.
 | `data.py` | Loads the bundled CSV and validates the schema. |
 | `features.py` | Builds causal rolling and lag features. |
 | `split.py` | Splits the feature frame by time only. |
-| `models.py` | Holds the detector registry and the scorers. |
+| `models.py` | Holds the detector registry, scorers and contributions. |
 | `evaluate.py` | Computes ranking and threshold metrics. |
+| `export.py` | Writes scores and flagged rows to CSV. |
 | `model_card.py` | Renders a Markdown model card. |
 | `report.py` | Writes a self-contained Plotly HTML report. |
 | `experiment.py` | Runs one experiment from start to finish. |
@@ -37,8 +38,8 @@ secret or network call is needed for the demo or for the tests.
 
 The experiment pulls these modules into one pipeline. The data step loads the
 CSV. The feature step adds causal rows. The split step cuts by time. The model
-step fits the detector. The evaluate step scores and flags rows. The report step
-writes the artifacts.
+step fits the detector and the contribution method. The evaluate step scores
+and flags rows. The report and export steps write the artifacts.
 
 ## Leakage-safety model
 
@@ -87,22 +88,47 @@ Or call the package as a module.
 python -m tiny_anomaly_lab --output output
 ```
 
-The command writes `output/report.html` and `output/model_card.md`. Open the
-report in a web browser. No server and no network are needed.
+The command writes `output/report.html`, `output/model_card.md`,
+`output/scores.csv` and `output/flagged_rows.csv`. Open the report in a web
+browser. No server and no network are needed.
 
 ## Detectors
 
-Two detectors ship with the lab.
+Three detectors ship with the lab.
 
 - `isolation_forest` wraps the scikit-learn Isolation Forest.
 - `zscore_rule` scores the largest standardised residual.
+- `mahalanobis` scores the correlation-aware Mahalanobis distance.
 
 Pick the detector with a flag.
 
 ```bash
-tiny-anomaly-lab --detector zscore_rule --output output
+tiny-anomaly-lab --detector mahalanobis --output output
 ```
 
+## Local feature contribution
+
+Each detector can attribute its row score to the input features. The lab uses
+this in two places: a contribution panel in the report and a `top_feature`
+column in the flagged-rows CSV.
+
+- `zscore_rule`: contribution is the absolute standardised residual. The
+  largest value marks the feature that drove the score.
+- `mahalanobis`: contribution is a signed exact split of the squared distance.
+  Row contributions sum to the squared score. A negative value shows a feature
+  that offsets the distance through correlation.
+- `isolation_forest`: no additive decomposition ships with the lab. The report
+  omits the contribution panel for this detector.
+
+## CSV export
+
+Two CSV files sit next to the report.
+
+- `scores.csv` holds every feature-frame row with its score, flag and a
+  `contrib_` column per feature.
+- `flagged_rows.csv` holds the flagged evaluation rows with the raw value
+  columns, the score, the threshold and the dominant contributor.
+
 ## Configuration
 
 | Flag | Default | Meaning |
@@ -139,6 +165,8 @@ ROC AUC: 0.7082  Average precision: 0.3970
 Flagged 9 rows in evaluation (TP=3, FP=6).
 Report: output\report.html
 Model card: output\model_card.md
+Scores: output\scores.csv
+Flagged rows: output\flagged_rows.csv
 ```
 
 This output comes from the z-score rule run.
@@ -149,11 +177,22 @@ ROC AUC: 0.7151  Average precision: 0.5175
 Flagged 4 rows in evaluation (TP=3, FP=1).
 ```
 
+This output comes from the Mahalanobis run.
+
+```text
+Detector: mahalanobis
+ROC AUC: 0.6938  Average precision: 0.4756
+Flagged 6 rows in evaluation (TP=3, FP=3).
+```
+
 A committed sample lives under `examples/sample_run/`. Open
-`examples/sample_run/report.html` to view the interactive figure.
+`examples/sample_run/report.html` to view the interactive figure. The
+`examples/sample_run_mahalanobis/` folder holds the same artifacts for the
+Mahalanobis detector, including a contribution panel and signed contributions
+in `flagged_rows.csv`.
 
 The model card records the detector, the data windows, the leakage controls, the
-metrics, the intended use and the limitations.
+metrics, the per-feature contribution method, the intended use and the limitations.
 
 ## Tests
 
@@ -170,14 +209,18 @@ ruff check .
 ```
 
 The suite covers data loading, causal features, the split, the metrics, the
-detectors, the experiment, the model card and the report.
+detectors, the per-feature contributions, the CSV export, the experiment, the
+model card and the report.
 
 ## Limitations
 
 The bundled dataset is synthetic and small. Results are illustrative. Isolation
 Forest scores shift when the random state or the scikit-learn version changes.
 Causal rolling features lag true change points by the window length. The
-detector assumes the calibration window is close to normal operation.
+detector assumes the calibration window is close to normal operation. The
+Mahalanobis covariance uses a shrinkage estimate that trades bias for stability
+on small windows, and its signed contributions can hide a feature behind a
+correlated partner.
 
 ## Project layout
 
@@ -194,10 +237,15 @@ examples/              Committed sample artifacts.
 
 Later releases stay independent of this one.
 
+Done in this release:
+
 - Add a multivariate detector beside the two current scorers.
-- Add a stream evaluation that replays rows one at a time.
-- Add local feature contribution for the z-score rule.
+- Add local feature contribution for the z-score rule and the new detector.
 - Add CSV export of the flagged rows and the scores.
+
+Still open:
+
+- Add a stream evaluation that replays rows one at a time.
 - Add a container image for a fully reproducible run.
 
 ## License
diff --git a/examples/sample_run/flagged_rows.csv b/examples/sample_run/flagged_rows.csv
new file mode 100644
index 0000000..7a9d148
--- /dev/null
+++ b/examples/sample_run/flagged_rows.csv
@@ -0,0 +1,10 @@
+timestamp,temperature,pressure,is_anomaly,score,threshold,top_feature,top_contribution
+2024-05-08T23:00:00.000000,20.6558,100.7938,0,0.009341502065532858,-1.973247953923618e-17,temperature_resid,0.0
+2024-05-11T22:00:00.000000,17.4403,102.5557,1,0.05595914490488485,-1.973247953923618e-17,temperature_resid,0.0
+2024-05-11T23:00:00.000000,18.0048,98.5259,1,0.06060271102137793,-1.973247953923618e-17,temperature_resid,0.0
+2024-05-12T00:00:00.000000,19.062,102.5169,1,0.014676161592953041,-1.973247953923618e-17,temperature_resid,0.0
+2024-05-12T21:00:00.000000,15.7843,100.8118,0,0.03643460189011305,-1.973247953923618e-17,temperature_resid,0.0
+2024-05-12T22:00:00.000000,17.0782,100.4592,0,0.025249986865079732,-1.973247953923618e-17,temperature_resid,0.0
+2024-05-12T23:00:00.000000,17.6438,100.8774,0,0.005199350558301674,-1.973247953923618e-17,temperature_resid,0.0
+2024-05-13T17:00:00.000000,14.0389,99.96,0,0.03554997145410932,-1.973247953923618e-17,temperature_resid,0.0
+2024-05-13T18:00:00.000000,14.735,100.764,0,0.013833939210193269,-1.973247953923618e-17,temperature_resid,0.0
diff --git a/examples/sample_run/model_card.md b/examples/sample_run/model_card.md
index b87f375..6a8c113 100644
--- a/examples/sample_run/model_card.md
+++ b/examples/sample_run/model_card.md
@@ -1,6 +1,6 @@
 # Model card: isolation_forest
 
-Generated: 2026-07-28 UTC.
+Generated: 2026-07-29 UTC.
 
 ## Overview
 
@@ -54,6 +54,10 @@ Derived features (8): temperature_resid, temperature_z, temperature_lag_1, tempe
 | true_positives | 3 |
 | false_positives | 6 |
 
+## Local feature contribution
+
+No native additive decomposition ships with the lab. The contribution panel is omitted for this detector.
+
 ## Intended use
 
 Use this card to reproduce a leakage-safe anomaly experiment on small time-series datasets. Treat the output as a teaching and review artifact, not as a production control.
@@ -64,3 +68,4 @@ Use this card to reproduce a leakage-safe anomaly experiment on small time-serie
 - Isolation Forest scores shift when the random state or scikit-learn version changes.
 - Causal rolling features lag true change points by the window length.
 - The detector assumes the calibration window is close to normal operation.
+- Mahalanobis contributions are signed; correlated features can offset each other.
diff --git a/examples/sample_run/report.html b/examples/sample_run/report.html
index 9b56c1d..4898044 100644
--- a/examples/sample_run/report.html
+++ b/examples/sample_run/report.html
@@ -2,6 +2,6 @@
 <head><meta charset="utf-8" /></head>
 <body>
     <div>                        <script type="text/javascript">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>
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diff --git a/examples/sample_run/scores.csv b/examples/sample_run/scores.csv
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diff --git a/examples/sample_run_mahalanobis/flagged_rows.csv b/examples/sample_run_mahalanobis/flagged_rows.csv
new file mode 100644
index 0000000..a1e0a3e
--- /dev/null
+++ b/examples/sample_run_mahalanobis/flagged_rows.csv
@@ -0,0 +1,7 @@
+timestamp,temperature,pressure,is_anomaly,score,threshold,top_feature,top_contribution
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+2024-05-12T23:00:00.000000,17.6438,100.8774,0,4.103791516001944,3.919082400694897,pressure_lag_24,13.187861934327385
diff --git a/examples/sample_run_mahalanobis/model_card.md b/examples/sample_run_mahalanobis/model_card.md
new file mode 100644
index 0000000..cd415ec
--- /dev/null
+++ b/examples/sample_run_mahalanobis/model_card.md
@@ -0,0 +1,71 @@
+# Model card: mahalanobis
+
+Generated: 2026-07-29 UTC.
+
+## Overview
+
+Use this card to reproduce a leakage-safe anomaly experiment on small time-series datasets. Treat the output as a teaching and review artifact, not as a production control.
+
+## Identity
+
+| Field | Value |
+| --- | --- |
+| Detector | mahalanobis |
+| Random state | 7 |
+| Rolling window (hours) | 6 |
+| Lags (hours) | 1, 24 |
+| Contamination | 0.05 |
+| Threshold source | calibration quantile |
+| Calibration anomalies dropped | True |
+
+## Data windows
+
+| Field | Value |
+| --- | --- |
+| Calibration rows | 156 |
+| Calibration start | 2024-05-02 00:00:00 |
+| Calibration end | 2024-05-08 11:00:00 |
+| Evaluation rows | 156 |
+| Evaluation start | 2024-05-08 12:00:00 |
+| Evaluation end | 2024-05-14 23:00:00 |
+| Evaluation anomalies | 10 |
+
+## Features
+
+Derived features (8): temperature_resid, temperature_z, temperature_lag_1, temperature_lag_24, pressure_resid, pressure_z, pressure_lag_1, pressure_lag_24.
+
+## Leakage controls
+
+- The detector is fit on the calibration window only.
+- The scaler is fit on the calibration window only.
+- The threshold comes from calibration scores, not evaluation labels.
+- All features use past and current rows only; future rows are never read.
+
+## Metrics (evaluation window)
+
+| Field | Value |
+| --- | --- |
+| roc_auc | 0.6938 |
+| average_precision | 0.4756 |
+| precision_at_k | 0.5 |
+| recall_at_k | 0.5 |
+| threshold | 3.919082 |
+| flagged | 6 |
+| true_positives | 3 |
+| false_positives | 3 |
+
+## Local feature contribution
+
+Per-feature contribution equals deviation times precision-weighted deviation. Row contributions sum to the squared Mahalanobis distance. Negative values show features that offset the distance through correlation.
+
+## Intended use
+
+Use this card to reproduce a leakage-safe anomaly experiment on small time-series datasets. Treat the output as a teaching and review artifact, not as a production control.
+
+## Limitations
+
+- The bundled dataset is synthetic and small; results are illustrative.
+- Isolation Forest scores shift when the random state or scikit-learn version changes.
+- Causal rolling features lag true change points by the window length.
+- The detector assumes the calibration window is close to normal operation.
+- Mahalanobis contributions are signed; correlated features can offset each other.
diff --git a/examples/sample_run_mahalanobis/report.html b/examples/sample_run_mahalanobis/report.html
new file mode 100644
index 0000000..693b59b
--- /dev/null
+++ b/examples/sample_run_mahalanobis/report.html
@@ -0,0 +1,7 @@
+<html>
+<head><meta charset="utf-8" /></head>
+<body>
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diff --git a/examples/sample_run_zscore/flagged_rows.csv b/examples/sample_run_zscore/flagged_rows.csv
new file mode 100644
index 0000000..f1f95a7
--- /dev/null
+++ b/examples/sample_run_zscore/flagged_rows.csv
@@ -0,0 +1,5 @@
+timestamp,temperature,pressure,is_anomaly,score,threshold,top_feature,top_contribution
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diff --git a/examples/sample_run_zscore/model_card.md b/examples/sample_run_zscore/model_card.md
index 3bec220..d01e019 100644
--- a/examples/sample_run_zscore/model_card.md
+++ b/examples/sample_run_zscore/model_card.md
@@ -1,6 +1,6 @@
 # Model card: zscore_rule
 
-Generated: 2026-07-28 UTC.
+Generated: 2026-07-29 UTC.
 
 ## Overview
 
@@ -54,6 +54,10 @@ Derived features (8): temperature_resid, temperature_z, temperature_lag_1, tempe
 | true_positives | 3 |
 | false_positives | 1 |
 
+## Local feature contribution
+
+Per-feature contribution is the absolute standardised residual. The largest value marks the feature that drove the row score.
+
 ## Intended use
 
 Use this card to reproduce a leakage-safe anomaly experiment on small time-series datasets. Treat the output as a teaching and review artifact, not as a production control.
@@ -64,3 +68,4 @@ Use this card to reproduce a leakage-safe anomaly experiment on small time-serie
 - Isolation Forest scores shift when the random state or scikit-learn version changes.
 - Causal rolling features lag true change points by the window length.
 - The detector assumes the calibration window is close to normal operation.
+- Mahalanobis contributions are signed; correlated features can offset each other.
diff --git a/examples/sample_run_zscore/report.html b/examples/sample_run_zscore/report.html
index 2f5f83f..ec0298e 100644
--- a/examples/sample_run_zscore/report.html
+++ b/examples/sample_run_zscore/report.html
@@ -2,6 +2,6 @@
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 <body>
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diff --git a/examples/sample_run_zscore/scores.csv b/examples/sample_run_zscore/scores.csv
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index 0000000..884a7a1
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diff --git a/tests/test_experiment.py b/tests/test_experiment.py
index e3ac27b..b603b1d 100644
--- a/tests/test_experiment.py
+++ b/tests/test_experiment.py
@@ -40,11 +40,30 @@ def test_run_experiment_writes_artifacts(tmp_path):
     r = run_experiment(ExperimentConfig(), output_dir=out)
     assert r.report_path is not None and r.report_path.exists()
     assert r.card_path is not None and r.card_path.exists()
+    assert r.scores_csv_path is not None and r.scores_csv_path.exists()
+    assert r.flagged_csv_path is not None and r.flagged_csv_path.exists()
     html = r.report_path.read_text(encoding="utf-8")
     assert "Tiny Anomaly Lab report" in html
     card = r.card_path.read_text(encoding="utf-8")
     assert "Model card" in card
     assert "Leakage controls" in card
+    assert "Local feature contribution" in card
+
+
+def test_run_experiment_contributions_align_to_features():
+    cfg = ExperimentConfig()
+    r = run_experiment(cfg)
+    assert r.contributions is not None
+    assert len(r.contributions) == r.feature_frame.height
+    assert all(len(row) == len(r.feature_columns) for row in r.contributions)
+
+
+def test_run_experiment_mahalanobis_is_deterministic():
+    cfg = ExperimentConfig(detector="mahalanobis")
+    a = run_experiment(cfg)
+    b = run_experiment(cfg)
+    assert a.scores == b.scores
+    assert a.metrics.as_dict() == b.metrics.as_dict()
 
 
 def test_run_experiment_rejects_bad_fraction():
diff --git a/tests/test_export.py b/tests/test_export.py
new file mode 100644
index 0000000..746451a
--- /dev/null
+++ b/tests/test_export.py
@@ -0,0 +1,111 @@
+from datetime import datetime, timedelta
+
+import polars as pl
+
+from tiny_anomaly_lab.export import export_flagged_csv, export_scores_csv
+
+
+def _frame(n: int = 6) -> pl.DataFrame:
+    ts = [datetime(2024, 1, 1) + timedelta(hours=i) for i in range(n)]
+    return pl.DataFrame(
+        {
+            "timestamp": ts,
+            "temperature": [10.0, 10.1, 50.0, 10.0, 10.1, 10.0],
+            "pressure": [1.0] * n,
+            "is_anomaly": [0, 0, 1, 0, 0, 0],
+            "temperature_resid": [0.0, 0.1, 40.0, 0.0, 0.1, 0.0],
+            "pressure_resid": [0.0] * n,
+        }
+    )
+
+
+def test_export_scores_csv_writes_all_rows_with_contributions(tmp_path):
+    frame = _frame()
+    feat_cols = ["temperature_resid", "pressure_resid"]
+    scores = [0.1, 0.1, 0.9, 0.1, 0.1, 0.1]
+    flags = [s > 0.5 for s in scores]
+    contributions = [
+        [0.1, 0.0],
+        [0.1, 0.0],
+        [40.0, 0.0],
+        [0.1, 0.0],
+        [0.1, 0.0],
+        [0.1, 0.0],
+    ]
+    out = tmp_path / "scores.csv"
+    path = export_scores_csv(frame, scores, flags, contributions, feat_cols, out)
+    assert path == out
+    written = pl.read_csv(out)
+    assert written.height == frame.height
+    assert set(written.columns) >= {
+        "timestamp",
+        "temperature",
+        "is_anomaly",
+        "score",
+        "flagged",
+        "contrib_temperature_resid",
+        "contrib_pressure_resid",
+    }
+    assert written["score"].to_list() == scores
+    assert written["flagged"].to_list() == [int(b) for b in flags]
+
+
+def test_export_scores_csv_omits_contribution_columns_when_none(tmp_path):
+    frame = _frame()
+    scores = [0.1] * frame.height
+    flags = [False] * frame.height
+    out = tmp_path / "scores.csv"
+    export_scores_csv(frame, scores, flags, None, [], out)
+    written = pl.read_csv(out)
+    assert "contrib_temperature_resid" not in written.columns
+
+
+def test_export_flagged_csv_writes_only_flagged_rows(tmp_path):
+    frame = _frame()
+    feat_cols = ["temperature_resid", "pressure_resid"]
+    scores = [0.1, 0.1, 0.9, 0.1, 0.1, 0.1]
+    flags = [False, False, True, False, False, False]
+    contributions = [
+        [0.1, 0.0],
+        [0.1, 0.0],
+        [40.0, 1.0],
+        [0.1, 0.0],
+        [0.1, 0.0],
+        [0.1, 0.0],
+    ]
+    out = tmp_path / "flagged_rows.csv"
+    path = export_flagged_csv(
+        frame, scores, flags, contributions, feat_cols, ["temperature", "pressure"], 0.5, out
+    )
+    assert path == out
+    written = pl.read_csv(out)
+    assert written.height == 1
+    assert written["temperature"].to_list() == [50.0]
+    assert written["top_feature"].to_list() == ["temperature_resid"]
+    assert written["top_contribution"].to_list() == [40.0]
+
+
+def test_export_flagged_csv_ignores_high_score_when_not_flagged(tmp_path):
+    """A calibration row whose score exceeds the threshold is not exported."""
+    frame = _frame()
+    scores = [0.9, 0.1, 0.9, 0.1, 0.1, 0.1]
+    flags = [False, False, True, False, False, False]
+    out = tmp_path / "flagged_rows.csv"
+    export_flagged_csv(
+        frame, scores, flags, None, [], ["temperature", "pressure"], 0.5, out
+    )
+    written = pl.read_csv(out)
+    assert written.height == 1
+    assert written["temperature"].to_list() == [50.0]
+
+
+def test_export_flagged_csv_empty_when_nothing_flagged(tmp_path):
+    frame = _frame()
+    scores = [0.1] * frame.height
+    flags = [False] * frame.height
+    out = tmp_path / "flagged_rows.csv"
+    export_flagged_csv(
+        frame, scores, flags, None, [], ["temperature", "pressure"], 0.5, out
+    )
+    written = pl.read_csv(out)
+    assert written.height == 0
diff --git a/tests/test_model_card.py b/tests/test_model_card.py
index 49d205b..725d45d 100644
--- a/tests/test_model_card.py
+++ b/tests/test_model_card.py
@@ -1,3 +1,5 @@
+from dataclasses import replace
+
 from tiny_anomaly_lab.model_card import CardInput, render_card
 
 
@@ -42,3 +44,9 @@ def test_render_card_markdown_tables():
     text = render_card(_card())
     assert "| Field | Value |" in text
     assert "| --- | --- |" in text
+
+
+def test_render_card_includes_explanation_method():
+    for detector in ("isolation_forest", "zscore_rule", "mahalanobis"):
+        card_text = render_card(replace(_card(), detector_name=detector))
+        assert "Local feature contribution" in card_text
diff --git a/tests/test_models.py b/tests/test_models.py
index e9eabd3..c9fd81a 100644
--- a/tests/test_models.py
+++ b/tests/test_models.py
@@ -1,4 +1,5 @@
 import numpy as np
+import pytest
 
 from tiny_anomaly_lab.models import REGISTRY, make_detector
 
@@ -9,7 +10,7 @@ def _calibration():
 
 
 def test_registry_exposes_expected_detectors():
-    assert set(REGISTRY) == {"isolation_forest", "zscore_rule"}
+    assert set(REGISTRY) == {"isolation_forest", "zscore_rule", "mahalanobis"}
 
 
 def test_isolation_forest_is_deterministic_with_seed():
@@ -42,7 +43,46 @@ def test_zscore_rule_flags_large_components():
 
 
 def test_unknown_detector_raises():
-    import pytest
-
     with pytest.raises(KeyError):
         make_detector("nope")
+
+
+def test_zscore_explain_marks_the_driving_feature():
+    X = _calibration()
+    det = make_detector("zscore_rule", random_state=0)
+    det.fit(X)
+    extreme = np.array([[0.0, 0.0, 30.0]])
+    contrib = det.explain_samples(extreme)
+    assert contrib.shape == (1, 3)
+    assert int(np.argmax(contrib[0])) == 2
+    scores = det.score_samples(extreme)
+    assert scores[0] == pytest.approx(contrib[0].max())
+
+
+def test_isolation_forest_explain_returns_zeros():
+    X = _calibration()
+    det = make_detector("isolation_forest", random_state=0)
+    det.fit(X)
+    contrib = det.explain_samples(X[:5])
+    assert contrib.shape == (5, 3)
+    assert np.all(contrib == 0.0)
+
+
+def test_mahalanobis_scores_outliers_higher():
+    X = _calibration()
+    det = make_detector("mahalanobis", random_state=0)
+    det.fit(X)
+    normal = np.array([[0.0, 0.0, 0.0]])
+    outlier = np.array([[20.0, 0.0, 0.0]])
+    assert det.score_samples(outlier)[0] > det.score_samples(normal)[0]
+
+
+def test_mahalanobis_explain_sums_to_squared_score():
+    X = _calibration()
+    det = make_detector("mahalanobis", random_state=0)
+    det.fit(X)
+    scores = det.score_samples(X)
+    contrib = det.explain_samples(X)
+    assert contrib.shape == X.shape
+    row_sums = contrib.sum(axis=1)
+    assert np.allclose(row_sums, scores**2)
diff --git a/tests/test_report.py b/tests/test_report.py
index 1618386..be41e06 100644
--- a/tests/test_report.py
+++ b/tests/test_report.py
@@ -29,6 +29,14 @@ def _input():
     )
 
 
+def _input_with_contributions():
+    base = _input()
+    feats = ["temperature_resid", "pressure_resid"]
+    contributions = [[0.0, 0.0]] * 10
+    contributions[3] = [40.0, 0.5]
+    return base, feats, contributions
+
+
 def test_render_report_writes_self_contained_html(tmp_path):
     out = tmp_path / "report.html"
     path = render_report(_input(), out)
@@ -43,3 +51,24 @@ def test_render_report_creates_parent_directory(tmp_path):
     out = tmp_path / "nested" / "deep" / "report.html"
     render_report(_input(), out)
     assert out.exists()
+
+
+def test_render_report_omits_contribution_panel_for_isolation_forest(tmp_path):
+    out = tmp_path / "report.html"
+    render_report(_input(), out)
+    html = out.read_text(encoding="utf-8")
+    assert "Mean abs. contribution" not in html
+
+
+def test_render_report_adds_contribution_panel_when_available(tmp_path):
+    base, feats, contributions = _input_with_contributions()
+    out = tmp_path / "report.html"
+    render_report(
+        base.__class__(
+            **{**base.__dict__, "feature_columns": feats, "contributions": contributions}
+        ),
+        out,
+    )
+    html = out.read_text(encoding="utf-8")
+    assert "Mean abs. contribution" in html
+    assert "temperature_resid" in html
diff --git a/tiny_anomaly_lab/__main__.py b/tiny_anomaly_lab/__main__.py
index 1bbea03..ac91d51 100644
--- a/tiny_anomaly_lab/__main__.py
+++ b/tiny_anomaly_lab/__main__.py
@@ -7,7 +7,7 @@
 
 from tiny_anomaly_lab.experiment import ExperimentConfig, run_experiment
 
-DETECTORS = ("isolation_forest", "zscore_rule")
+DETECTORS = ("isolation_forest", "zscore_rule", "mahalanobis")
 
 
 def _build_parser() -> argparse.ArgumentParser:
@@ -51,6 +51,10 @@ def main(argv: list[str] | None = None) -> int:
         print(f"Report: {result.report_path}")
     if result.card_path:
         print(f"Model card: {result.card_path}")
+    if result.scores_csv_path:
+        print(f"Scores: {result.scores_csv_path}")
+    if result.flagged_csv_path:
+        print(f"Flagged rows: {result.flagged_csv_path}")
     return 0
 
 
diff --git a/tiny_anomaly_lab/experiment.py b/tiny_anomaly_lab/experiment.py
index d868486..19415df 100644
--- a/tiny_anomaly_lab/experiment.py
+++ b/tiny_anomaly_lab/experiment.py
@@ -20,6 +20,7 @@
 from tiny_anomaly_lab.data import load_bundled, load_csv
 from tiny_anomaly_lab.data import value_columns as _value_cols
 from tiny_anomaly_lab.evaluate import Metrics, calibration_threshold, evaluate
+from tiny_anomaly_lab.export import export_flagged_csv, export_scores_csv
 from tiny_anomaly_lab.features import FeatureSpec, build_features, feature_columns
 from tiny_anomaly_lab.model_card import CardInput, render_card
 from tiny_anomaly_lab.models import make_detector
@@ -57,8 +58,12 @@ class ExperimentResult:
     calibration_size: int
     threshold: float
     card: str
+    feature_columns: list[str]
+    contributions: list[list[float]] | None = None
     report_path: Path | None = None
     card_path: Path | None = None
+    scores_csv_path: Path | None = None
+    flagged_csv_path: Path | None = None
 
 
 def _timestamps(frame: pl.DataFrame, start: int, end: int) -> tuple[str, str]:
@@ -106,6 +111,8 @@ def run_experiment(
         )
     metrics = evaluate(y_eval, scores_all[cut:], threshold)
 
+    contrib_all = detector.explain_samples(X)
+
     flags = [False] * feats.height
     for i in range(cut, feats.height):
         flags[i] = bool(scores_all[i] > threshold)
@@ -134,6 +141,8 @@ def run_experiment(
 
     report_path = None
     card_path = None
+    scores_csv_path = None
+    flagged_csv_path = None
     if output_dir is not None:
         out = Path(output_dir)
         out.mkdir(parents=True, exist_ok=True)
@@ -149,8 +158,28 @@ def run_experiment(
             calibration_end=cal_end,
             evaluation_start=eval_start,
             threshold=threshold,
+            feature_columns=feat_cols,
+            contributions=contrib_all.tolist(),
         )
         report_path = render_report(report_input, out / "report.html")
+        scores_csv_path = export_scores_csv(
+            feats,
+            scores_all.tolist(),
+            flags,
+            contrib_all.tolist(),
+            feat_cols,
+            out / "scores.csv",
+        )
+        flagged_csv_path = export_flagged_csv(
+            feats,
+            scores_all.tolist(),
+            flags,
+            contrib_all.tolist(),
+            feat_cols,
+            vals,
+            threshold,
+            out / "flagged_rows.csv",
+        )
 
     return ExperimentResult(
         metrics=metrics,
@@ -160,6 +189,10 @@ def run_experiment(
         calibration_size=cut,
         threshold=threshold,
         card=card,
+        feature_columns=feat_cols,
+        contributions=contrib_all.tolist(),
         report_path=report_path,
         card_path=card_path,
+        scores_csv_path=scores_csv_path,
+        flagged_csv_path=flagged_csv_path,
     )
diff --git a/tiny_anomaly_lab/export.py b/tiny_anomaly_lab/export.py
new file mode 100644
index 0000000..d13fcbc
--- /dev/null
+++ b/tiny_anomaly_lab/export.py
@@ -0,0 +1,99 @@
+"""Write the experiment rows, scores and contributions to CSV.
+
+The lab ships two artifacts next to the report and the model card:
+
+- `scores.csv`: every feature-frame row with its score, flag and per-feature
+  contribution columns. Use it to reproduce plots outside the browser.
+- `flagged_rows.csv`: the evaluation rows the detector flagged. Each row keeps
+  the raw value columns, the score, the dominant feature and its contribution.
+
+Both functions write with Polars and return the path they wrote to. They never
+read the evaluation window to pick a threshold; they only record what the
+experiment already produced.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import polars as pl
+
+
+def _contribution_columns(feature_columns: list[str]) -> list[str]:
+    return [f"contrib_{c}" for c in feature_columns]
+
+
+def export_scores_csv(
+    frame: pl.DataFrame,
+    scores: list[float],
+    flags: list[bool],
+    contributions: list[list[float]] | None,
+    feature_columns: list[str],
+    out_path: str | Path,
+) -> Path:
+    """Write all rows with score, flag and per-feature contributions."""
+    out_path = Path(out_path)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    cols = ["timestamp", *frame.columns[1:]]
+    df = frame.select([c for c in cols if c in frame.columns])
+    df = df.with_columns(
+        pl.Series("score", scores),
+        pl.Series("flagged", [int(b) for b in flags]),
+    )
+    if contributions is not None and feature_columns:
+        columns = {feat: [row[k] for row in contributions] for k, feat in enumerate(feature_columns)}
+        for name, feat in zip(
+            _contribution_columns(feature_columns), feature_columns, strict=True
+        ):
+            df = df.with_columns(pl.Series(name, columns[feat]))
+    df.write_csv(out_path)
+    return out_path
+
+
+def export_flagged_csv(
+    frame: pl.DataFrame,
+    scores: list[float],
+    flags: list[bool],
+    contributions: list[list[float]] | None,
+    feature_columns: list[str],
+    value_columns: list[str],
+    threshold: float,
+    out_path: str | Path,
+) -> Path:
+    """Write the flagged evaluation rows with their dominant contributor.
+
+    The `flags` list is the experiment's evaluation-only flag vector, so
+    calibration rows are never exported here even if their score exceeds the
+    threshold.
+    """
+    out_path = Path(out_path)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    flagged_idx = [i for i, b in enumerate(flags) if b]
+    n = len(flagged_idx)
+    keep = ["timestamp", *value_columns, "is_anomaly"]
+
+    def _slice(values: list) -> list:
+        return [values[i] for i in flagged_idx]
+
+    columns: dict[str, list] = {}
+    for c in keep:
+        if c in frame.columns:
+            columns[c] = _slice(frame[c].to_list())
+    columns["score"] = _slice(scores)
+    columns["threshold"] = [threshold] * n
+    if contributions is not None and feature_columns:
+        top_feature: list[str] = []
+        top_contribution: list[float] = []
+        for i in flagged_idx:
+            row = contributions[i]
+            j = int(max(range(len(feature_columns)), key=lambda k: abs(row[k])))
+            top_feature.append(feature_columns[j])
+            top_contribution.append(float(row[j]))
+        columns["top_feature"] = top_feature
+        columns["top_contribution"] = top_contribution
+    if not columns:
+        pl.DataFrame().write_csv(out_path)
+        return out_path
+    df = pl.DataFrame(columns)
+    df.write_csv(out_path)
+    return out_path
diff --git a/tiny_anomaly_lab/model_card.py b/tiny_anomaly_lab/model_card.py
index 30eb9cd..207559e 100644
--- a/tiny_anomaly_lab/model_card.py
+++ b/tiny_anomaly_lab/model_card.py
@@ -50,8 +50,25 @@ class CardInput:
     "Isolation Forest scores shift when the random state or scikit-learn version changes.",
     "Causal rolling features lag true change points by the window length.",
     "The detector assumes the calibration window is close to normal operation.",
+    "Mahalanobis contributions are signed; correlated features can offset each other.",
 ]
 
+EXPLANATION_METHODS: dict[str, str] = {
+    "isolation_forest": (
+        "No native additive decomposition ships with the lab. The contribution "
+        "panel is omitted for this detector."
+    ),
+    "zscore_rule": (
+        "Per-feature contribution is the absolute standardised residual. The "
+        "largest value marks the feature that drove the row score."
+    ),
+    "mahalanobis": (
+        "Per-feature contribution equals deviation times precision-weighted "
+        "deviation. Row contributions sum to the squared Mahalanobis distance. "
+        "Negative values show features that offset the distance through correlation."
+    ),
+}
+
 
 def _table(rows: Iterable[tuple[str, str]]) -> str:
     lines = ["| Field | Value |", "| --- | --- |"]
@@ -119,6 +136,13 @@ def render_card(card: CardInput) -> str:
         "",
         _table(metrics_rows),
         "",
+        "## Local feature contribution",
+        "",
+        EXPLANATION_METHODS.get(
+            card.detector_name,
+            "This detector ships no per-feature contribution method.",
+        ),
+        "",
         "## Intended use",
         "",
         INTENDED_USE,
diff --git a/tiny_anomaly_lab/models.py b/tiny_anomaly_lab/models.py
index e3f3761..9c1851f 100644
--- a/tiny_anomaly_lab/models.py
+++ b/tiny_anomaly_lab/models.py
@@ -3,9 +3,25 @@
 Each detector exposes a uniform interface:
     fit(X)           - learn parameters from a calibration frame
     score_samples(X) - return larger-is-more-anomalous scores (float array)
+    explain_samples(X) - return per-feature contributions to the score
 
-A `StandardScaler` is fit on the calibration frame only, so detectors that rely
+A `StandardScaler` is fit on the calibration frame only. Detectors that rely
 on scaled features never see the evaluation window during fitting.
+
+Local feature contribution
+--------------------------
+`explain_samples` returns an array shaped `(n_rows, n_features)` that
+attributes the row score to the input feature columns. Two semantics ship
+with the lab:
+
+- `zscore_rule`: the absolute standardised residual per feature. The largest
+  contribution marks the feature that drove the score.
+- `mahalanobis`: a signed exact decomposition whose row sum equals the squared
+  Mahalanobis distance. Negative values show features that offset the distance
+  through correlation.
+
+`isolation_forest` has no native additive decomposition in this lab. Its
+`explain_samples` returns zeros, so reports skip its contribution panel.
 """
 
 from __future__ import annotations
@@ -14,6 +30,7 @@
 from typing import Protocol
 
 import numpy as np
+from sklearn.covariance import LedoitWolf
 from sklearn.ensemble import IsolationForest
 from sklearn.preprocessing import StandardScaler
 
@@ -25,6 +42,8 @@ def fit(self, X: np.ndarray) -> Detector: ...
 
     def score_samples(self, X: np.ndarray) -> np.ndarray: ...
 
+    def explain_samples(self, X: np.ndarray) -> np.ndarray: ...
+
 
 @dataclass
 class IsolationForestDetector:
@@ -53,6 +72,10 @@ def score_samples(self, X: np.ndarray) -> np.ndarray:
         # sklearn's decision_function grows with normality; invert it.
         return -self._model.decision_function(self._scaler.transform(X))
 
+    def explain_samples(self, X: np.ndarray) -> np.ndarray:
+        # The forest has no native additive per-feature attribution here.
+        return np.zeros((X.shape[0], X.shape[1]), dtype=float)
+
 
 @dataclass
 class ZscoreDetector:
@@ -77,10 +100,49 @@ def score_samples(self, X: np.ndarray) -> np.ndarray:
         scaled = self._scaler.transform(X)
         return np.max(np.abs(scaled), axis=1)
 
+    def explain_samples(self, X: np.ndarray) -> np.ndarray:
+        # Per-feature anomaly evidence. The score is the max of these values.
+        scaled = self._scaler.transform(X)
+        return np.abs(scaled)
+
+
+@dataclass
+class MahalanobisDetector:
+    """Correlation-aware multivariate distance from the calibration norm.
+
+    The score is the Mahalanobis distance under a shrinkage estimate of the
+    covariance matrix. The shrinkage keeps the estimate stable on the small
+    calibration windows the lab targets. The detector is explainable: the
+    squared distance decomposes exactly into signed per-feature contributions
+    that capture how each feature pulls the row toward, or away from, the norm.
+    """
+
+    random_state: int = 0
+    name: str = "mahalanobis"
+
+    def __post_init__(self) -> None:
+        self._cov = LedoitWolf(assume_centered=False)
+
+    def fit(self, X: np.ndarray) -> MahalanobisDetector:
+        self._cov.fit(X)
+        return self
+
+    def score_samples(self, X: np.ndarray) -> np.ndarray:
+        squared = self._cov.mahalanobis(X)
+        return np.sqrt(np.maximum(squared, 0.0))
+
+    def explain_samples(self, X: np.ndarray) -> np.ndarray:
+        precision = self._cov.get_precision()
+        deviation = X - self._cov.location_
+        # d^T S^-1 d = sum_i d_i * (S^-1 d)_i, so the row sum equals the squared score.
+        projected = deviation @ precision
+        return deviation * projected
+
 
 REGISTRY: dict[str, type] = {
     "isolation_forest": IsolationForestDetector,
     "zscore_rule": ZscoreDetector,
+    "mahalanobis": MahalanobisDetector,
 }
 
 
diff --git a/tiny_anomaly_lab/report.py b/tiny_anomaly_lab/report.py
index 360c423..afbffb8 100644
--- a/tiny_anomaly_lab/report.py
+++ b/tiny_anomaly_lab/report.py
@@ -1,8 +1,10 @@
 """Render a self-contained Plotly HTML report from an experiment run.
 
-The report contains two stacked panels:
+The report contains two or three stacked panels:
   - the value series with true anomalies marked;
-  - the anomaly score series with flagged evaluation points marked.
+  - the anomaly score series with flagged evaluation points marked;
+  - when the detector supplies per-feature contributions, a bar chart of the
+    mean absolute contribution per feature over the flagged evaluation rows.
 
 Plotly offline writes the file with no external network call.
 """
@@ -28,12 +30,24 @@ class ReportInput:
     calibration_end: str
     evaluation_start: str
     threshold: float
+    feature_columns: list[str] | None = None
+    contributions: list[list[float]] | None = None
 
 
 def _row_axis(note: str) -> list[dict]:
     return [{"text": note, "showarrow": False, "xref": "paper", "yref": "paper"}]
 
 
+def _has_useful_contributions(contributions: list[list[float]] | None) -> bool:
+    if not contributions:
+        return False
+    total = 0.0
+    for row in contributions:
+        for v in row:
+            total += abs(v)
+    return total > 0.0
+
+
 def render_report(data: ReportInput, out_path: str | Path) -> Path:
     """Write a self-contained HTML report and return its path."""
     out_path = Path(out_path)
@@ -43,13 +57,19 @@ def render_report(data: ReportInput, out_path: str | Path) -> Path:
         pl.Series("score", data.scores),
         pl.Series("flagged", [int(b) for b in data.flags]),
     )
+    show_contrib = _has_useful_contributions(data.contributions) and bool(data.feature_columns)
+    n_rows = 3 if show_contrib else 2
+    row_heights = [0.5, 0.3, 0.2] if show_contrib else [0.62, 0.38]
+    subplot_titles = ["Sensor values and labelled anomalies", "Anomaly score"]
+    if show_contrib:
+        subplot_titles.append("Mean abs. contribution per feature (flagged rows)")
     fig = make_subplots(
-        rows=2,
+        rows=n_rows,
         cols=1,
-        shared_xaxes=True,
-        vertical_spacing=0.12,
-        row_heights=[0.62, 0.38],
-        subplot_titles=("Sensor values and labelled anomalies", "Anomaly score"),
+        shared_xaxes=False,
+        vertical_spacing=0.10,
+        row_heights=row_heights,
+        subplot_titles=tuple(subplot_titles),
     )
 
     for col in data.value_columns:
@@ -113,20 +133,44 @@ def render_report(data: ReportInput, out_path: str | Path) -> Path:
             annotation_position="top left",
         )
     if data.calibration_end and data.evaluation_start:
-        fig.add_vrect(
-            x0=data.calibration_end,
-            x1=data.evaluation_start,
-            fillcolor="lightgray",
-            opacity=0.25,
-            line_width=0,
-            annotation_text="split",
-            annotation_position="top left",
+        for split_row in (1, 2):
+            fig.add_vrect(
+                x0=data.calibration_end,
+                x1=data.evaluation_start,
+                fillcolor="lightgray",
+                opacity=0.25,
+                line_width=0,
+                annotation_text="split" if split_row == 1 else "",
+                annotation_position="top left",
+                layer="below",
+                row=split_row,
+                col=1,
+            )
+
+    if show_contrib:
+        flagged_idx = [i for i, b in enumerate(data.flags) if b]
+        feats = data.feature_columns or []
+        means = [0.0] * len(feats)
+        if flagged_idx:
+            for j in range(len(feats)):
+                means[j] = sum(abs(data.contributions[i][j]) for i in flagged_idx) / len(flagged_idx)
+        fig.add_trace(
+            go.Bar(
+                x=feats,
+                y=means,
+                name="mean |contribution|",
+                marker={"color": "steelblue"},
+                showlegend=False,
+            ),
+            row=3,
+            col=1,
         )
 
     metric_text = "  ".join(f"{k}={v}" for k, v in data.metrics.items())
+    height = 820 if show_contrib else 720
     fig.update_layout(
         title=f"Tiny Anomaly Lab report - {data.detector_name}",
-        height=720,
+        height=height,
         template="simple_white",
         legend={"orientation": "h", "y": -0.18},
         annotations=[
@@ -146,7 +190,7 @@ def render_report(data: ReportInput, out_path: str | Path) -> Path:
             ),
         ],
     )
-    fig.update_xaxes(matches="x")
+    fig.update_xaxes(matches="x", row=2, col=1)
     html = fig.to_html(
         full_html=True,
         include_plotlyjs="cdn",