Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking

Machine-learned models are often described as "black boxes". In many real-world applications however, models may have to sacrifice predictive power in favour of human-interpretability. When this is the case, feature engineering becomes a crucial task, which requires significant and time-consuming human effort. Whilst some features are inherently static, representing properties that cannot be influenced (e.g., the age of an individual), others capture characteristics that could be adjusted (e.g., the daily amount of carbohydrates taken). Nonetheless, once a model is learned from the data, each prediction it makes on new instances is irreversible - assuming every instance to be a static point located in the chosen feature space. There are many circumstances however where it is important to understand (i) why a model outputs a certain prediction on a given instance, (ii) which adjustable features of that instance should be modified, and finally (iii) how to alter such a prediction when the mutated instance is input back to the model. In this paper, we present a technique that exploits the internals of a tree-based ensemble classifier to offer recommendations for transforming true negative instances into positively predicted ones. We demonstrate the validity of our approach using an online advertising application. First, we design a Random Forest classifier that effectively separates between two types of ads: low (negative) and high (positive) quality ads (instances). Then, we introduce an algorithm that provides recommendations that aim to transform a low quality ad (negative instance) into a high quality one (positive instance). Finally, we evaluate our approach on a subset of the active inventory of a large ad network, Yahoo Gemini.Comment: 10 pages, KDD 201

An Interactive Visual Tool to Enhance Understanding of Random Forest Predictions

Random forests are known to provide accurate predictions, but the predictions are not easy to understand. In order to provide support for understanding such predictions, an interactive visual tool has been developed. The tool can be used to manipulate selected features to explore “what-if” scenarios. It exploits the internal structure of decision trees in a trained forest model and presents this information as interactive plots and charts. In addition, the tool presents a simple decision rule as an explanation for the prediction. It also presents the recommendation for reassignments of feature values of the example that leads to change in the prediction to a preferred class. An evaluation of the tool was undertaken in a large truck manufacturing company, targeting the fault prediction of a selected component in trucks. A set of domain experts were invited to use the tool and provide feedback in post-task interviews. The result of this investigation suggests that the tool indeed may aid in understanding the predictions of a random forest, and also allows for gaining new insights

Causality concepts in machine learning: heterogeneous treatment effect estimation with machine learning & model interpretation with counterfactual and semi-factual explanations

Over decades, machine learning and causality were two separate research fields that developed independently of each other. It was not until recently that the exchange between the two intensified. This thesis comprises seven articles that contribute novel insights into the utilization of causality concepts in machine learning and highlights how both fields can benefit from one another. One part of this thesis focuses on adapting machine learning algorithms for estimating heterogeneous treatment effects. Specifically, random forest-based methods have demonstrated to be a powerful approach to heterogeneous treatment effect estimation; however, understanding the key elements responsible for that remains an open question. To provide answers, one contribution analyzed which elements of two popular forest-based heterogeneous treatment effect estimators – causal forests and model-based forests – are beneficial in case of real-valued outcomes. A simulation study reveals that model-based forests' simultaneous split selection based on prognostic and predictive effects is effective for randomized controlled trials, while causal forests' orthogonalization strategy is advantageous for observational data under confounding. Another contribution shows that combining these elements yields a versatile model framework applicable to a wide range of application cases: observational data with diverse outcome types, potentially under different forms of censoring. Another part focuses on two methods that leverage causality concepts to interpret machine learning models: counterfactual explanations and semi-factual explanations. Counterfactual explanations describe minimal changes in a few features required for changing a prediction, while semi-factual explanations describe maximal changes in a few features required for not changing a prediction. These insights are valuable because they reveal which features do or do not affect a prediction, and they can help to object against or justify a prediction. The existence of multiple equally good counterfactual explanations and semi-factual explanations for a given instance is often overlooked in the existing literature. This is also pointed out in the first contribution of the second part, which deals with possible pitfalls of interpretation methods, potential solutions, and open issues. To address the multiplicity of counterfactual explanations and semi-factual explanations, two contributions propose methods to generate multiple explanations: The underlying optimization problem was formalized multi-objectively for counterfactual explanations and as a hyperbox search for semi-factual explanations. Both approaches can be easily adapted to other use cases, with another contribution demonstrating how the multi-objective approach can be applied to assess counterfactual fairness. Despite the multitude of counterfactual methods proposed in recent years, the availability of methods for users of the programming language R remains extremely limited. Therefore, another contribution introduces a modular R package that facilitates the application and comparison of multiple counterfactual explanation methods.Über Jahrzehnte waren maschinelles Lernen und Kausalität zwei getrennte Forschungsbereiche, die sich unabhängig voneinander entwickelten. Erst in jüngster Zeit hat sich der Austausch zwischen den beiden Bereichen intensiviert. Diese Arbeit umfasst sieben Artikel, die neue Einblicke in die Nutzung von Kausalitätskonzepten im maschinellen Lernen geben, und zeigt, wie beide Bereiche voneinander profitieren können. Ein Teil dieser Arbeit befasst sich mit der Anpassung von Algorithmen des maschinellen Lernens zur Schätzung heterogener Behandlungseffekte. Insbesondere Random-Forest-Methoden haben sich als leistungsfähiger Ansatz für die Behandlungseffekt-Schätzung erwiesen; das Verständnis der Schlüsselelemente, die dafür verantwortlich sind, bleibt jedoch eine offene Frage. Um Antworten zu finden, wurde in einem Beitrag analysiert, welche Elemente von zwei beliebten Random-Forest-Schätzern - Causal Forests und Model-based Forests - im Fall von reellwertigen Zielvariablen von Vorteil sind. Eine Simulationsstudie zeigt, dass die gleichzeitige Split-Auswahl von Model-based Forests auf der Grundlage von prognostischen und prädiktiven Effekten für randomisierte kontrollierte Studien effektiv ist, während die Orthogonalisierungsstrategie der Causal Forests für Beobachtungsdaten mit Confoundern von Vorteil ist. Ein weiterer Beitrag zeigt, dass die Kombination dieser Elemente ein vielseitiges Framework für Modelle ergibt, welches auf viele verschiedene Fälle anwendbar ist: Beobachtungsdaten mit verschiedenen Arten von Zielvariablen, möglicherweise unter verschiedenen Formen von Zensierung. Ein weiterer Teil dieser Arbeit konzentriert sich auf zwei Methoden, die Kausalitätskonzepte zur Interpretation von Modellen des maschinellen Lernens nutzen: Counterfactual Explanations (kontrafaktische Erklärungen) und Semi-factual Explanations (semi-faktische Erklärungen). Counterfactual Explanations beschreiben minimale Änderungen in einigen wenigen Merkmalen, die für die Änderung einer Vorhersage erforderlich sind, während Semi-factual Explanations maximale Änderungen in einigen wenigen Merkmalen beschreiben, die zu keiner Änderung der Vorhersage führen. Diese Erkenntnisse sind wertvoll, weil sie zeigen, welche Merkmale eine Vorhersage beeinflussen und welche nicht, und sie können helfen, eine Vorhersage zu widerlegen oder zu rechtfertigen. Die Existenz mehrerer gleich guter Counterfactual Explanations und Semi-factual Explanations für einen Datenpunkt wird in der bestehenden Literatur oft übersehen. Darauf weist auch der erste Beitrag des zweiten Teils hin, der sich mit möglichen Fallstricken von Interpretationsmethoden, möglichen Lösungen und offenen Fragen befasst. Um der Vielzahl von Counterfactual Explanations und Semi-factual Explanations zu begegnen, werden in zwei Beiträgen Methoden zur Generierung multipler Erklärungen vorgeschlagen: Das zugrundeliegende Optimierungsproblem wurde für Counterfactual Explanations multi-objektiv und für Semi-factual Explanations als Hyperbox-Suche formalisiert. Beide Ansätze können leicht an andere Anwendungsfälle angepasst werden, wobei ein weiterer Beitrag zeigt, wie der multi-objektive Ansatz zur Bewertung der Modellfairness im kontrafaktischen Sinne angewendet werden kann. Trotz der Vielzahl von Counterfactual Explanations Methoden, die in den letzten Jahren vorgeschlagen wurden, ist die Verfügbarkeit von Methoden für Nutzer der Programmiersprache R äußerst begrenzt. Daher wird in einem weiteren Beitrag ein modulares R-Paket vorgestellt, das die Anwendung und den Vergleich mehrerer Counterfactual Explanations Methoden erleichtert

Multi-Objective Counterfactual Explanations

Counterfactual explanations are one of the most popular methods to make predictions of black box machine learning models interpretable by providing explanations in the form of `what-if scenarios'. Most current approaches optimize a collapsed, weighted sum of multiple objectives, which are naturally difficult to balance a-priori. We propose the Multi-Objective Counterfactuals (MOC) method, which translates the counterfactual search into a multi-objective optimization problem. Our approach not only returns a diverse set of counterfactuals with different trade-offs between the proposed objectives, but also maintains diversity in feature space. This enables a more detailed post-hoc analysis to facilitate better understanding and also more options for actionable user responses to change the predicted outcome. Our approach is also model-agnostic and works for numerical and categorical input features. We show the usefulness of MOC in concrete cases and compare our approach with state-of-the-art methods for counterfactual explanations

counterfactuals: An R Package for Counterfactual Explanation Methods

Counterfactual explanation methods provide information on how feature values of individual observations must be changed to obtain a desired prediction. Despite the increasing amount of proposed methods in research, only a few implementations exist whose interfaces and requirements vary widely. In this work, we introduce the counterfactuals R package, which provides a modular and unified R6-based interface for counterfactual explanation methods. We implemented three existing counterfactual explanation methods and propose some optional methodological extensions to generalize these methods to different scenarios and to make them more comparable. We explain the structure and workflow of the package using real use cases and show how to integrate additional counterfactual explanation methods into the package. In addition, we compared the implemented methods for a variety of models and datasets with regard to the quality of their counterfactual explanations and their runtime behavior

Optimal Counterfactual Explanations in Tree Ensembles

Counterfactual explanations are usually generated through heuristics that are sensitive to the search's initial conditions. The absence of guarantees of performance and robustness hinders trustworthiness. In this paper, we take a disciplined approach towards counterfactual explanations for tree ensembles. We advocate for a model-based search aiming at "optimal" explanations and propose efficient mixed-integer programming approaches. We show that isolation forests can be modeled within our framework to focus the search on plausible explanations with a low outlier score. We provide comprehensive coverage of additional constraints that model important objectives, heterogeneous data types, structural constraints on the feature space, along with resource and actionability restrictions. Our experimental analyses demonstrate that the proposed search approach requires a computational effort that is orders of magnitude smaller than previous mathematical programming algorithms. It scales up to large data sets and tree ensembles, where it provides, within seconds, systematic explanations grounded on well-defined models solved to optimality.Comment: Authors Accepted Manuscript (AAM), to be published in the Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 2021. Additional typo corrections. Open source code available at https://github.com/vidalt/OCEA