=Paper=
{{Paper
|id=Vol-3017/xcbr101
|storemode=property
|title=Actionable Feature Discovery in Counterfactuals using Feature Relevance Explainers
|pdfUrl=https://ceur-ws.org/Vol-3017/101.pdf
|volume=Vol-3017
|authors=Nirmalie Wiratunga,Anjana Wijekoon,Ikechukwu Nkisi-Orji,Kyle Martin,Chamath Palihawadana,David Corsar
|dblpUrl=https://dblp.org/rec/conf/iccbr/WiratungaWNMPC21
}}
==Actionable Feature Discovery in Counterfactuals using Feature Relevance Explainers==
https://ceur-ws.org/Vol-3017/101.pdf
Actionable Feature Discovery in Counterfactuals
using Feature Relevance Explainers�
Nirmalie Wiratunga, Anjana Wijekoon, Ikechukwu Nkisi-Orji, Kyle Martin,
Chamath Palihawadana, and David Corsar
School of Computing, Robert Gordon University, Aberdeen AB10 7GJ, Scotland, UK
{n.wiratunga, a.wijekoon1, i.nkisi-orji, k.martin3, c.palihawadana,
d.corsar1}@rgu.ac.uk
Abstract. Counterfactual explanations focus on “actionable knowledge”
to help end-users understand how a Machine Learning model outcome
could be changed to a more desirable outcome. For this purpose a coun-
terfactual explainer needs to be able to reason with similarity knowledge
in order to discover input dependencies that relate to outcome changes.
Identifying the minimum subset of feature changes to action a change in
the decision is an interesting challenge for counterfactual explainers. In
this paper we show how feature relevance based explainers (i.e. LIME,
SHAP), can inform a counterfactual explainer to identify the minimum
subset of “actionable features”. We demonstrate our DisCERN (Discov-
ering Counterfactual Explanations using Relevance Features from Neigh-
bourhoods) algorithm on three datasets and compare against the widely
used counterfactual approach DiCE. Our preliminary results show that
DisCERN to be a viable strategy that should be adopted to minimise
the actionable changes.
Keywords: Explainable AI, Counterfactual, Feature Relevance, Action-
able Features
1 Introduction
Understanding a user’s explanation need is central to a system’s capability of
provisioning an explanation which satisfies that need [7]. Typically an explana-
tion generated by an AI system is considered to convey the internal state or
workings of an algorithm that resulted in the system’s decision [14]. In Machine
Learning (ML) the decision tends to be a discrete label or class (or in the case of
regression tasks a numeric value). Although explanations focused on the internal
state or logic of the algorithm are helpful to ML researchers they are arguably
less useful to an end-user who may be more interested in how their current cir-
cumstances could be changed to receive a desired (better) outcome in the future.
�
This research is funded by the iSee project (https://isee4xai.com/) which received
funding from EPSRC under the grant number EP/V061755/1. iSee is part of the
CHIST-ERA pathfinder programme for European coordinated research on future
and emerging information and communication technologies.
Copyright © 2021 for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 Anjana Wijekoon et al.
This calls for explanations that focus on discovering relationships between the
input dependencies that led to the system’s decision.
Nearest-Like Neighbour (NLN) based explainer, could focus on input depen-
dencies by identifying similarity relationships between the current problem and
the retrieved nearest neighbour [5]. Research has shown that when similarity
computation is focused on feature selection and weighting, it can significantly
improve retrieval of NLN [16, 15]. Accordingly it would be reasonable to expect
that NLN-based explanation generation would also benefit from the knowledge
of feature importance. Certainly having to focus on a few key features in do-
mains with large numbers of features is likely to improve the cognitive burden
of understanding NLN-based explanations.
Unlike NLN based explanations, a counterfactual explanation focuses on
identifying “actionable knowledge”; which is knowledge about important causal
dependencies between the input and the ML decision. Such knowledge helps to
understand what could be changed in the input to achieve a preferred (desired)
decision outcome. Typically a Nearest-Unlike-Neighbour (NUN) is used to iden-
tify the number of differences between the input and its neighbour, that when
changed can lead to a change in the system’s decision [4]. A key challenge that we
address in this paper is to identify the minimum subset of feature value changes
to achieve a change in the decision - the “actionable features”. In this paper,
we discover the minimum actionable feature changes using feature relevance-
based explainer methods like LIME [10] or SHAP [6] which we introduce as the
DisCERN algorithm.
The rest of the paper is organised as follows. Section 2 investigates the im-
portance of counterfactual XAI and discusses key counterfactual methods and
evaluation methodologies. Section 3 presents the DisCERN algorithm to improve
the discovery of actionable features in counterfactuals. Section 4 presents the
evaluation methodologies, datasets and performance metrics. Section 5 presents
the evaluation results. Finally we draw conclusions and discuss future work in
Section 6.
2 Related Work
Like many explanation methods, counterfactual explanations are rooted within
the study of human psychology. Counterfactual thinking is a mental exercise
where we attempt to isolate the specific actions which contributed to a (usually
undesirable) outcome, with the goal of identifying how we could alter these
facts to reach an alternative (and often more desirable) outcome [11]. In this
manner we derive a form of causal explanation for the outcome that was actually
achieved, allowing us to reason about how a different outcome could be achieved
in future [3]. To clarify, consider the following fictitious example of a runner
who was placed third in a race: “I won the bronze medal for my race (actual
outcome), but I would have won the gold medal (desired outcome) if I hadn’t
tripped (causal action which changed outcome)”. Through this thought process,
the runner has derived what they believe to be a causal action that led to
Actionable Feature Discovery using Feature Relevance Explainers 3
receiving the bronze medal. With that knowledge inferred, the runner can then
reason that in order to achieve a better outcome, they should run a similar race
again, but avoid tripping. Likewise with a counterfactual explanation we aim
to explain why the system generated a specific outcome instead of another, by
inferring causal relationships between input features [14].
Case-based Reasoning (CBR) [4] and optimisation techniques [9, 13] have
been the pillars of discovering counterfactuals from data. Recent work in CBR
has shown how counterfactual case generation can be conveniently supported
through the case adaptation stage, where query-retrieval pairs of successful coun-
terfactual explanation experiences are used to create an explanation casebase [4].
NICE [1] is another CBR and NUN based approach which performs an exhaus-
tive search to discover the counterfactual by incrementally copying the features
of the NUN to the query and checking for class change. Unlike the CBR ap-
proach to counterfactual generation, DiCE [9] trains a generative model using
gradient descent optimisation to output multiple perturbed diverse counterfac-
tuals. With the CBR approach additional counterfactuals can be identified by
increasing the neighbourhood. This ability to provide multiple counterfactuals
has been found to improve the end-user’s mental model. In our work we also
adopt CBR’s NUN method to find counterfactuals but instead of the adaptation
CBR step or exhaustive search in NICE we opt for feature relevance explainers
to inform us on actionable feature discovery. In doing so we avoid the need to cre-
ate similarity based explanation case bases [4] or perform exhaustive search [1]
yet maintain the advantage of locality-based explanations which ensure valid
counterfactuals that are often harder to guarantee with optimisation methods.
Notably, our approach is contrasting to the feature relevance explainer proposed
in [8] where counterfactuals generated from optimisation based methods are used
to determine feature weights.
Quantitative evaluation of counterfactual explanations focus on measures
that can ascertain properties of good counterfactuals. Here explanatory com-
petency is defined as the competency of the explainer CBR system to solve
any future query. It is measured by the fraction of queries that are currently
explained by the explainer CBR system - coverage of the casebase. In contrast,
authors of the DiCE algorithm proposed two performance measures to evaluate a
counterfactual as proximity and sparsity [9]. Sparsity and proximity refers to the
number of feature changes and amount of change needed to achieve class change.
They also proposed two additional measures to evaluate multiple counterfactuals
as validity and diversity. Validity measures if the counterfactuals presented by
the method actually belong to the desirable class (i.e. not the same class as the
query). Diversity measures the heterogeneity between multiple counterfactuals.
Plausibility is another quantitative measure which checks the validity of a gen-
erated counterfactual with respect to the boundaries of real features [1]. We find
validity, diversity and plausibility are inapt for our work because 1) by selecting
a NUN we ensure 100% validity; 2) we are only selecting a single counterfactual
which invalidates a measure for diversity; and 3) a DisCERN counterfactual is
derived from a real data instance (NUN) to ensure 100% plausibility. However,
4 Anjana Wijekoon et al.
Fig. 1: Nearest Like and Unlike neighbours in a 2D features space (m = 2)
we find proximity and sparsity can be adopted to compare different counterfac-
tual methods and measure the efficiency of actionable feature discovery.
3 Methods
The DisCERN algorithm uses feature relevance to identify the minimum subset
of changes needed to form a counterfactual explanation from a retrieved NUN.
Here we formalise the NUN counterfactual approach and thereafter discuss how
weights from Feature Relevance Explainers can be used in DisCERN to discover
actionable features.
3.1 Nearest-Unlike-Neighbour Counterfactual
The goal of a counterfactual explanation is to guide the end-user to achieve
class change (i.e. actionable), with a focus on minimising the number of changes
needed to flip the decision to a more desirable outcome for the end-user. Given
a query instance, a data instance qualifies as a NUN only if the instance is the
nearest neighbour of the query and belongs to a different class from the query.
Formally, given a query instance, x = {f1 , f2 , ..., fm }, its counterfactual,
x̂ = {fˆ1 , fˆ2 , ..., fˆm }, is identified as the NUN in the feature space [4] (Figure 1).
Here fi is the feature value at index i and m is the number of features used to
represent instances.
Usually the distance between instances is calculated using Euclidean distance.
Other distance measures include Manhattan distance and inverse Cosine simi-
larity. Discovering the minimal number of actionable features (from a maximum
of m potential feature changes) is one of the main challenges for counterfac-
tual explainers. With DisCERN, we address this challenge by exploiting Feature
Relevance Explainers.
3.2 Feature Relevance Explainers
LIME [10] is a model-agnostic feature relevance explainer which creates an
interpretable model around a given data instance, p to estimate how each feature
Actionable Feature Discovery using Feature Relevance Explainers 5
contributed to the black-box model outcome. LIME creates a set of perturbations
within the neighbourhood of p and they are labelled using the black-box model.
This new labelled dataset is used to create a linear interpretable model. The
resulting surrogate model is interpretable and only locally faithful to the black-
box model (i.e. correctly classifies the data instance p but not all data instances).
The new interpretable model is used to predict the classification outcome of p
that needs to be explained and obtain the weights that indicate how each feature
contributed to the outcome.
SHAP [6] is a model-agnostic feature relevance explainer which demonstrated
improved computational performance and better consistency compared to LIME.
SHAP is based on the shapley regression values introduced in game theory [12].
Shapley values are calculated by creating linear models using subsets of features
present in p. More specifically, a model is trained with a subset of features of
size m� and another model is trained with a subset of features of size m� + m̂.
Here m� + m̂ <= m and the second model additionally includes a set of features
m̂ selected from the set of features that were left out in the first model. A set of
such model pairs are created for all possible feature combinations. For a given
data instance p, that needs to be explained, the prediction differences of these
model pairs are averaged to find the explainable feature relevance weights.
3.3 Feature weights from Feature Relevance Explainers
Feature Relevance Explainers provide a feature relevance weights vector for any
given data instance. In DisCERN, we use the magnitude of the relevance weights
as a method to order features. Formally, the output of a feature relevance ex-
plainer is a vector of weights, w = (w1 , w2 , . . . , wm ), where wi is a real-valued
weight assigned to feature, fi . A positive weight (wi >= 0) indicates that the
corresponding feature contributes positively and a negative weight (wi < 0) cor-
responds negatively towards the predicted outcome. These relevance weights are
used to define an ordering on features. Given a weights vector, w, the overall
value, w(.), is the weight lookup of a feature included in x. This is used to define
the ordering � for features:
fi �w fj if and only if w(fi ) ≥ w(fj ) (1)
According to [6], there are three desirable properties of a feature relevance
explainer: local accuracy, missingness and consistency. Local accuracy guaran-
tees that the surrogate model and black-box model predicts the same outcome
for a given data instance. Missingness ensures that there there is no weight con-
tribution from a missing feature (i.e. wi = 0 if fi = 0). Consistency refers to the
quality whereby an explanation is reproducible (i.e the same data instance results
in the same explanation). While LIME satisfies local accuracy and missingness,
SHAP satisfied all three properties. Both methods can be used to provide the
weights vector, w, for a query, or its NUN.
6 Anjana Wijekoon et al.
(a) Query Importance (b) NUN Importance
Fig. 2: Actionable Feature Discovery with QI and NI
3.4 Actionable Feature Discovery with Feature Relevance
Explainers
The number of feature changes, n, required to achieve a class change, can range
from 1 to m (1 <= n <= m). We propose two methods to discover actionable
features, with the goal of minimising the number of feature changes (n) needed
for a succinct yet actionable explanation. The first method is to replace the values
of the most important features in the query with the corresponding feature values
from its NUN; or a second alternative is to identify the most relevant features
of the NUN and reuse those feature values in the modified query instance.
We illustrate these two methods in Figure 2. Here, we depict an example with
5 features where we replace features in the query until a class change is observed.
In the left, the query features are ordered by their feature weights and the most
important features are replaced by the respective NUN feature values (QI). In
the right, the NUN features are ordered by their feature weights and the most
important features are reused by the query (NI). QI and NI achieve class change
with 2 and 3 feature replacements respectively.
3.5 DisCERN: Discovering Counterfactual Explanations with
Relevance Features from NUNs
Algorithm 1 brings together methods from Sections 3.1- 3.4 to recognise action-
able features to generate NUN counterfactuals. Here y = F(x) is the classifier
prediction for the query and, ŷ = F(x̂) is the class prediction for the NUN.
Relevance weights from Section 3.3 are identified in reference to either the query
or the NUN, which we have denoted as p; and the Order method provides a list
of feature indices ranked by the relevance weights. Feature values are iteratively
replaced until the actionable change condition is met (i.e., y �= y � |y � = F (x� ))
or the query is completely changed to the NUN (which is guaranteed to re-
sult in class change). Thus DisCERN returns x� as the counterfactual. Clearly
the fewer replacement iterations needed the better the actionable features being
discovered.
Actionable Feature Discovery using Feature Relevance Explainers 7
Algorithm 1 DisCERN [RelExp, p]
Require: (x, y): query and label pair
Require: (x̂, ŷ): NUN and label pair
Require: F: classification model
Require: p: either x or x̂ � as described in Section 3.4
Require: RelExp: Feature Relevance Explainer
1: W = RelExp(p) � see Equation 1
2: Ŵ = Order(W )
3: Initialise y � = y; x� = x � init counterfactual as query
4: for wi ∈ Ŵ do
5: if fi� �= fˆi then � i is the index of wi
6: fi� = fˆi � copy the NUN feature value to the query
7: y � = F(x� ) � class prediction for perturbed query
8: if y � �= y then � check for class change
9: Break
10: end if
11: end if
12: end for
13: return x� � returns the counterfactual
4 Evaluation
The goal of this evaluation is twofold. First a comparable study investigates dif-
ferent feature ordering strategies to find the most effective for actionable feature
discovery. Second a comparative evaluation determines the effectiveness of the
actionable feature discovery for counterfactual creation. We compare the coun-
terfactual creation methods QI and NI with DiCE [9] and to a random feature
ordering baseline.
4.1 Evaluation Methodology
We compare feature ordering heuristics using feature relevance knowledge from:
1. LIME and SHAP: feature weights from feature relevance explainers discussed
in Section 3.2.
2. LIMEC and SHAPC : these are two class level feature relevance explainer
versions of LIME and SHAP weights respectively, where for each class, the
aggregated feature relevance is the mean feature relevance weights over all
training data instances for that class.
3. Chi2: Chi-Squared feature selection method applied on the dataset and fea-
tures ordered by p-value.
Experiments for actionable feature discovery compare the following methods:
1. DisCERN [RND,Null]: A randomly ordered set of feature indices are used
in Algorithm DisCERN. Note that p is unspecified (i.e. null) with random
feature ordering.
8 Anjana Wijekoon et al.
2. DiCE: Optimisation based counterfactual explainer method [9]
3. DisCERN [SHAP,QI]: Query Feature Importance (QI) using SHAP feature
ordering
4. DisCERN [SHAP,NI]: NUN Feature Importance (NI) using SHAP feature
ordering
4.2 Datasets
Experiments are carried out on 3 datasets as follows: feature ordering com-
parative study is conducted using the Moodle dataset and results presented in
Section 5.1; and the actionable feature discovery comparative study is conducted
using the Moodle dataset as well as the Loan-2015 and Alcohol datasets, with
results presented in Section 5.2.
Moodle Dataset is constructed from records of student footprints on the
Moodle Virtual Learning Environment (VLE) for a single class delivered within
Robert Gordon University. VLE interactions help to capture vital touchpoints
that can be used as proxy measures of student engagement. The dataset consists
of 74 students who were enrolled for a Computer Science class during Semester
1 of 2020/2021 at RGU. The dataset contains 95 features, where each feature
is a learning resource stored on the Moodle VLE and the feature value is the
number of times it was accessed by a student.
The ML task is to predict if a student gets a higher or a lower grade based
on their Moodle footprint. This task is based on the assumption that there is a
causal relationship between the Moodle access and the final grade of a student.
We consider grades A and B as Higher grades and C, D, E and F as Lower
grades. Grades were consolidated as Higher and Lower to mitigate the compa-
rably lower number of data instances and class imbalance. This formed a dataset
of 74 instances for a binary classification task. A RandomForest classifier of 500
trees was used to predict the grade based on the Moodle footprint. The classifier
achieved 83% accuracy over three stratified folds. Note that when explaining an
outcome, we assume that the classifier has correctly predicted the grade.
The explanation intent explored is of type Why student A did not receive
a grade X? instead of Why did student A receive grade Y? The latter can be
explained using a feature relevance explainer presenting the contribution of the
most important features for the predicted grade; and the former Why not type
question explained through a counterfactual explanation to guide the student to
achieve a more desirable outcome in the future.
Loan-2015 dataset is the subset of 2015 records from the Lending Club loan
dataset on kaggle 1 . We limit the dataset to records from 2015 to create the
loan-2015 dataset of 421,095 data instances with 151 features. In this paper we
consider all features as actionable. However, this dataset include features such
1
https://www.kaggle.com/wordsforthewise/lending-club
Actionable Feature Discovery using Feature Relevance Explainers 9
as income, home ownership and length of employment that are considered non-
actionable in the real-world. The ML task is to predict if a loan will be fully
paid or not and this outcome is used to accept or reject future loan requests. We
apply data pre-processing steps recommended by the data providers to obtain
a dataset with 342,865 instances and 115 features to perform binary classifica-
tion. A RandomForest classifier with 500 trees achieved a 97% accuracy over
three stratified folds. The desirable outcome for an end-user is accepting a loan
request (i.e. similar users successfully re-paid their loans). For example an ex-
planation request can take the form of Why person A did not receive the loan?
and a counterfactual can guide the end-user to make necessary adjustments to
receive a desirable outcome in future.
Alcohol Dataset is the Blood Alcohol Concentration (BAC) dataset which
consists of 127,800 data instances with 5 features [2]. It includes features such
as gender, if a meal was taken, the duration between the meal and BAC test.
For the experiments in this paper we consider all features as actionable. The
ML task for this dataset is to predict if the BAC is over a regulatory limit.
Accordingly, in a pre-processing step the dataset is converted in to a binary
classification task by recognising the two classes with the BAC regulatory limit
as the decision threshold. A RandomForest classifier achieved 99% accuracy with
the resulting dataset over three stratified folds. Similar to previous two dataset
this use case also presents a desirable outcome for the end-user which is to
maintain the BAC below the threshold. Accordingly, counterfactual explanations
are sought by individuals who have a BAC above the threshold and are looking
to understand how they might keep their BAC below the threshold by better
managing one or more actionable features.
4.3 Performance Measures
In this paper we present two performance metrics to perform a quantitative
evaluation of the DisCERN algorithm. We note that these measures correspond
to sparsity and proximity in [9], but are not identical.
Mean number of feature changes (#F ) required to achieve class change is
calculated as follows:
�N � m
1
#F = 1ˆ (2)
N × m j=1 i=1 [fi �=fi ]
Here the number of features with different values between the counterfactual (x̂)
and the query (x) are calculated and averaged; where N refers to the number of
query instances, and m is the number of features.
10 Anjana Wijekoon et al.
Table 1: Comparison of feature ordering strategies
#F $F
DisCERN [,] QI NI QI NI
LIME 8.14 8.61 0.2642 0.2726
LIMEC 11.41 10.38 0.2308 0.2524
SHAP 7.69 8.32 0.2660 0.2454
SHAPC 10.28 10.18 0.2085 0.2068
Chi2 12.27 0.2700
Mean amount of feature changes ($F ) required to achieve class change is
calculated as follows:
�N � m
1
$F = (|fˆi − fi |) (3)
N × #F j=1 i=1
Here the sum of feature differences are averaged over #F and the number of
query instances (N ). All continuous features are min/max normalised and there-
fore, continuous feature differences are between 0 and 1 whereas categorical fea-
ture differences are always 1 (using the overlap distance). Accordingly, datasets
with more categorical features will have higher $F value, which means that the
$F measure is not comparable across datasets.
5 Results
5.1 Comparison of Feature Ordering Strategies
A comparison of DisCERN settings with 5 alternative options for RelExp; and 2
alternatives for p appear in Table 1 using the Moodle dataset. Each alternative’s
performance is compared on #F and $F . Note that there is no difference between
QI and NI when using Chi2 because the feature ordering applies to the entire
dataset. The comparison of feature ordering strategies show that SHAP achieves
the best performance over LIME and Chi2 with both QI and NI methods (see
bold font). SHAP using the QI feature ordering method has achieved lowest #F ,
whilst SHAPC has lowest $F . However, since SHAPC requires additional feature
changes to achieve class change, we consider SHAP to be a preferable strategy.
Moreover, we observe that LIME also achieves comparable performances for
both #F and $F . Notably, Chi2 failed to outperform both LIME and SHAP
strategies in both minimising number of features and amount of change. Overall,
these results emphasise the importance of feature relevance explainers as a proxy
to identifying features important to achieve class change.
5.2 Evaluation of Actionable Feature Discovery
Table 2 provides a comparison of our DisCERN counterfactual algorithm with
Random and DiCE. Note that in DisCERN, QI and NI are using SHAP as the
Actionable Feature Discovery using Feature Relevance Explainers 11
Table 2: Comparison of counterfactual methods on #F
DisCERN [SHAP,]
Dataset(total no of features) DisCERN [RND,Null] DiCE
QI NI
Moodle(95) 21.62 10.21 7.69 8.32
Loan-2015(115) 21.911 2.59 6.86 5.51
Alcohol(5) 2.16 2.53 2.11 2.12
Table 3: Comparison of counterfactual methods on $F
DisCERN [SHAP,]
Dataset DisCERN [RND,Null] DiCE
QI NI
Moodle 0.2924 0.6344 0.2660 0.2454
Loan-2015 0.0569 0.7763 0.0760 0.0711
Alcohol 0.0909 0.6707 0.0929 0.0925
feature ordering strategy. Results suggests that DisCERN with QI achieves the
best performance on the Moodle and Alcohol datasets and DiCE achieves best
performance with the Loan-2015 dataset (see bold font). DisCERN with QI and
NI achieve comparable performances across all three datasets which resembles
findings in Table 1. Interestingly, DiCE failed to outperform Random feature
ordering on the Alcohol dataset which could be due to the limited amount of
features available.
Results in Table 3 indicate that with DisCERN (with either QI or NI) we
also achieve class changes with lowest $F values (see bold font). It is unusual
that DisCERN with Random ordering resulted in lowest $F on the Loan-2015
dataset. $F performance of DisCERN with QI and NI is better compared to
DiCE on all three datasets. For instance, for a query in the Loan-2015 dataset,
the total amount of change with the DiCE method is 2.01(2.59 × 0.7763) and
with QI is 0.46(6.93 × 0.0660). In situations where actionable features are not
“easy to change”, it is more feasible to use DisCERN over DiCE.
6 Conclusion
In this paper, we presented a novel approach to finding actionable knowledge
when constructing an explanation using a counterfactual. We used feature rele-
vance explainers as a strategy to discover features that are most significant to a
predicted class and then used that knowledge to discover the actionable features
to achieve class change with minimal change. We demonstrated our approach
DisCERN using three datasets one of which (Moodle Dataset) is an original
contribution.
Our empirical results showed that SHAP is the most optimal feature rele-
vance explainer for ordering actionable features. Comparison of the QI and NI
counterfactual methods introduced in this paper have either outperformed or
achieved comparable performance over DiCE. The results have also highlighted
12 Anjana Wijekoon et al.
the need to find balance between the number of feature changes and amount of
feature change based on the selected actionable features. However, we find there
is conclusive evidence that feature relevance explainers are an important proxy
to discovering actionable features and minimising the changes required. Future
work will expand upon our evaluation to include additional real-world datasets
and the use of qualitative evaluation through crowd-sourcing techniques.
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