Deep Learning has become a very valuable tool in different fields, and no one doubts the learning capacity of these models. Nevertheless, since Deep Learning models are often seen as black boxes due to their lack of interpretability, there is a general mistrust in their decision-making process. To find a balance between effectiveness and interpretability, Explainable Artificial Intelligence (XAI) is gaining popularity in recent years, and some of the methods within this area are used to generate counterfactual explanations. The process of generating these explanations generally consists of solving an optimization problem for each input to be explained, which is unfeasible when real-time feedback is needed. To speed up this process, some methods have made use of autoencoders to generate instant counterfactual explanations. Recently, a method called Deep Guided Counterfactual Explanations (DGCEx) has been proposed, which trains an autoencoder attached to a classification model, in order to generate straightforward counterfactual explanations. However, this method does not ensure that the generated counterfactual instances are close to the data manifold, so unrealistic counterfactual instances may be generated. To overcome this issue, this paper presents Distribution Aware Deep Guided Counterfactual Explanations (DA-DGCEx), which adds a term to the DGCEx cost function that penalizes out of distribution counterfactual instances.
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
The problem of fair classification can be mollified if we develop a method to remove the embedded sensitive information from the classification features. This line of separating the sensitive information is developed through the causal inference, and the causal inference enables the counterfactual generations to contrast the what-if case of the opposite sensitive attribute. Along with this separation with the causality, a frequent assumption in the deep latent causal model defines a single latent variable to absorb the entire exogenous uncertainty of the causal graph. However, we claim that su…
Offline Imitation Learning with Variational Counterfactual Reasoning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；School of Engineering, Westlake University（西湖大学工学院）；DiDi Chuxing（滴滴出行）
Conditional Variational Auto Encoders (VAE) are gathering significant attention as an Explainable Artificial Intelligence (XAI) tool. The codes in the latent space provide a theoretically sound way to produce counterfactuals, i.e. alterations resulting from an intervention on a targeted semantic feature. To be applied on real images more complex models are needed, such as Hierarchical CVAE. This comes with a challenge as the naive conditioning is no longer effective. In this paper we show how relaxing the effect of the posterior leads to successful counterfactuals and we introduce VAEX an Hier…
Max Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）；Saarland University（萨尔大学）
In this paper, we introduce VACA, a novel class of variational graph autoencoders for causal inference in the absence of hidden confounders, when only observational data and the causal graph are available. Without making any parametric assumptions, VACA mimics the necessary properties of a Structural Causal Model (SCM) to provide a flexible and practical framework for approximating interventions (do-operator) and abduction-action-prediction steps. As a result, and as shown by our empirical results, VACA accurately approximates the interventional and counterfactual distributions on diverse SCMs…
University of Technology Sydney（悉尼科技大学）；Curtin University（科廷大学）
Counterfactual explanation is a form of interpretable machine learning that generates perturbations on a sample to achieve the desired outcome. The generated samples can act as instructions to guide end users on how to observe the desired results by altering samples. Although state-of-the-art counterfactual explanation methods are proposed to use variational autoencoder (VAE) to achieve promising improvements, they suffer from two major limitations: 1) the counterfactuals generation is prohibitively slow, which prevents algorithms from being deployed in interactive environments; 2) the counter…
We present a novel generative method for producing unseen and plausible counterfactual examples for reinforcement learning (RL) agents based upon outcome variables that characterize agent behavior. Our approach uses a variational autoencoder to train a latent space that jointly encodes information about the observations and outcome variables pertaining to an agent's behavior. Counterfactuals are generated using traversals in this latent space, via gradient-driven updates as well as latent interpolations against cases drawn from a pool of examples. These include updates to raise the likelihood …
From Baseline to Follow-Up: Counterfactual Spine DXA Image Synthesis in UK Biobank Using a Causal Hierarchical Variational Autoencoder
School of Electronics and Computer Science（电子与计算机科学学院）；University of Southampton（萨塞克斯大学）；MRC Lifecourse Epidemiology Centre（英国医学研究理事会生命周期流行病学中心）；University of Southampton, Southampton General Hospital（萨塞克斯大学索马塞特医院）；Computer Science University of Southampton（计算机科学萨塞克斯大学）
Counterfactual explanation is a common class of methods to make local explanations of machine learning decisions. For a given instance, these methods aim to find the smallest modification of feature values that changes the predicted decision made by a machine learning model. One of the challenges of counterfactual explanation is the efficient generation of realistic counterfactuals. To address this challenge, we propose VCNet-Variational Counter Net-a model architecture that combines a predictor and a counterfactual generator that are jointly trained, for regression or classification tasks. VC…
Counterfactual Explanations for Medical Image Classification and Regression using Diffusion Autoencoder
Technical University of Munich（慕尼黑工业大学）；Klinikum rechts der Isar（右侧伊萨尔医院）；Institute for Artificial Intelligence and Computer Science in Medicine, Technical University of Munich（慕尼黑工业大学人工智能与医学计算机科学研究所）；Computer Aided Medical Procedures, Technical University of Munich（慕尼黑工业大学计算机辅助医疗程序研究所）；AI for Image-Guided Diagnosis and Therapy, Technical University of Munich（慕尼黑工业大学图像引导诊断与治疗人工智能研究所）
In the environment of fair lending laws and the General Data Protection Regulation (GDPR), the ability to explain a model's prediction is of paramount importance. High quality explanations are the first step in assessing fairness. Counterfactuals are valuable tools for explainability. They provide actionable, comprehensible explanations for the individual who is subject to decisions made from the prediction. It is important to find a baseline for producing them. We propose a simple method for generating counterfactuals by using gradient descent to search in the latent space of an autoencoder a…
Rice University（莱斯大学）；Texas A&M University（得克萨斯农工大学）；J.P. Morgan AI Research（摩根大通人工智能研究院）
Counterfactual, serving as one emerging type of model explanation, has attracted tons of attentions recently from both industry and academia. Different from the conventional feature-based explanations (e.g., attributions), counterfactuals are a series of hypothetical samples which can flip model decisions with minimal perturbations on queries. Given valid counterfactuals, humans are capable of reasoning under ``what-if'' circumstances, so as to better understand the model decision boundaries. However, releasing counterfactuals could be detrimental, since it may unintentionally leak sensitive i…
Counterfactual instances offer human-interpretable insight into the local behaviour of machine learning models. We propose a general framework to generate sparse, in-distribution counterfactual model explanations which match a desired target prediction with a conditional generative model, allowing batches of counterfactual instances to be generated with a single forward pass. The method is flexible with respect to the type of generative model used as well as the task of the underlying predictive model. This allows straightforward application of the framework to different modalities such as ima…
University of Virginia（弗吉尼亚大学）；Bytedance AI Lab（字节跳动人工智能实验室）；J.P. Morgan AI Research（摩根大通人工智能研究院）
Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted label?". The comparison of this instance before and after perturbation can enhance human interpretation. Most existing studies on counterfactual explanations are limited in tabular data or image data. In this work, we study the problem of counterfactual explanation generation on graphs. A few studies have explored counterfactual explanations on graphs, but many challenges of this problem are still not well-addressed: 1)…
University of Tuebingen（图宾根大学）；Schufa Holding AG（Schufa控股股份公司）
Counterfactual explanations can be obtained by identifying the smallest change made to a feature vector to qualitatively influence a prediction; for example, from 'loan rejected' to 'awarded' or from 'high risk of cardiovascular disease' to 'low risk'. Previous approaches often emphasized that counterfactuals should be easily interpretable to humans, motivating sparse solutions with few changes to the feature vectors. However, these approaches would not ensure that the produced counterfactuals be proximate (i.e., not local outliers) and connected to regions with substantial data density (i.e.,…
MASCOTS: Model-Agnostic Symbolic COunterfactual explanations for Time Series
Warsaw University of Technology（华沙理工大学）；University of Warsaw（华沙大学）；University of Pisa（比萨大学）；ISTI-CNR（意大利国家研究委员会信息科学与技术研究所）；KP Labs（KP实验室）；European Space Operations Centre（欧洲空间操作中心）；ETH Zürich（苏黎世联邦理工学院）
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Amira Learning（Amira学习）；CognitiveScale（认知规模）
Counterfactual explanations have substantially increased in popularity in the past few years as a useful human-centric way of understanding individual black-box model predictions. While several properties desired of high-quality counterfactuals have been identified in the literature, three crucial concerns: the speed of explanation generation, robustness/sensitivity and succinctness of explanations (sparsity) have been relatively unexplored. In this paper, we present FASTER-CE: a novel set of algorithms to generate fast, sparse, and robust counterfactual explanations. The key idea is to effici…
Counterfactual Explanations for Deep Two-Sample Testing
Hasso-Plattner-Institute, University of Potsdam（波茨坦大学洪堡-劳恩堡研究所）；Hasso Plattner Institute for Digital Health at Mount Sinai Icahn School of Medicine at Mount Sinai（辛辛那提医学院洪堡数字健康研究所）
Variational autoencoders (VAEs) and other generative methods have garnered growing interest not just for their generative properties but also for the ability to dis-entangle a low-dimensional latent variable space. However, few existing generative models take causality into account. We propose a new decoder based framework named the Causal Counterfactual Generative Model (CCGM), which includes a partially trainable causal layer in which a part of a causal model can be learned without significantly impacting reconstruction fidelity. By learning the causal relationships between image semantic la…
Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments
School of New Media and Communication, Tianjin University（天津大学新媒体与传播学院）；Department of Computer Science, Baylor University（贝勒大学计算机科学系）；GE Aerospace Research（GE航空航天研究院）；NEC Labs America（美国 NEC 实验室）
IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer Study
organization= Department of Physics \& Astronomy, University of British Columbia , city= Vancouver , state= BC , country= Canada；organization= Department of Integrative Oncology, BC Cancer Research Institute , city= Vancouver , state= BC , country= Canada；organization= Department of Radiology, University of British Columbia , city= Vancouver , state= BC , country= Canada
L3S Research Center（L3S研究中心）；Leibniz Universität Hannover（汉诺威莱布尼茨大学）；University of Southern Denmark（南丹麦大学）；Freie Universität Berlin（柏林自由大学）
The importance of the neighborhood for training a local surrogate model to approximate the local decision boundary of a black box classifier has been already highlighted in the literature. Several attempts have been made to construct a better neighborhood for high dimensional data, like texts, by using generative autoencoders. However, existing approaches mainly generate neighbors by selecting purely at random from the latent space and struggle under the curse of dimensionality to learn a good local decision boundary. To overcome this problem, we propose a progressive approximation of the neig…
DR-VIDAL -- Doubly Robust Variational Information-theoretic Deep Adversarial Learning for Counterfactual Prediction and Treatment Effect Estimation on Real World Data
University of Pittsburgh（匹兹堡大学）；University of Florida（佛罗里达大学）
