Add short documentation.

docs/value/shapley.md

@@ -78,6 +78,50 @@ stop condition. This is an instance of a
 [MaxTime][pydvl.value.stopping.MaxTime] and
 [AbsoluteStandardError][pydvl.value.stopping.AbsoluteStandardError].
 
+### Classwise Shapley
+
+**Classwise Shapley** [@schoch_csshapley_2022] is different Shapley schema applicable
+for classification problems. The key insight is that samples can be beneficial
+for overall performance, while being detrimental for their own class. CS-Shapley changes
+the utility to account for this effect by decomposing it into a product of two
+functions. It is the in-class accuracy multiplied by a discounter out-of-class accuracy. 
+The value is defined as:
+
+$$
+v_u(x_i) \approx \frac{1}{K \cdot L}
+\sum_{S^{(k)}_{-y_i} \subseteq T_{-y_i} \setminus \{i\}}
+\sum_{\sigma^{(l)} \in \Pi(T_{y_i} \setminus \{i\})}
+[u( \sigma_{\colon i} \cup \{i\} | S_{-y_i} )
+− u( \sigma_{\colon i} |  S_{-y_i})]
+$$
+
+where $K$ is the number of subsets $S^{(k)}_{-y_i}$ sampled from the class complement
+set $T_{-y_i}$ of class c and $L$ is the number of permutations sampled from the class
+indices set $T_{y_i}$. The scoring function used has the form
+
+$$u(S_{y_i}|S_{-y_i}) = a_S(D_{y_i}))) \exp\{a_S(D_{-y_i}))\}.$$
+
+This can be further customised, but that form is shown by the authors to have certain
+desirable properties.
+
+
+```python
+from pydvl.utils import Dataset, Utility
+from pydvl.value import compute_classwise_shapley_values, ClasswiseScorer, HistoryDeviation
+from pydvl.value.shapley.truncated import RelativeTruncation
+
+model = ...
+scoring = ClasswiseScorer("accuracy")
+data = Dataset(...)
+utility = Utility(model, data, scoring)
+values = compute_classwise_shapley_values(
+    utility,
+    done=HistoryDeviation(n_steps=500, rtol=1e-3),
+    truncation=RelativeTruncation(utility, rtol=0.01),
+    n_resample_complement_sets=10,
+    normalize_values=True
+)
+```
 
 ### Owen sampling