This work presents CounterNet, a novel end-to-end learning framework which integrates Machine Learning (ML) model training and the generation of corresponding counterfactual (CF) explanations into a single end-to-end pipeline. Counterfactual explanations offer a contrastive case, i.e., they attempt to find the smallest modification to the feature values of an instance that changes the prediction of the ML model on that instance to a predefined output. Prior techniques for generating CF explanations suffer from two major limitations: (i) all of them are post-hoc methods designed for use with proprietary ML models -- as a result, their procedure for generating CF explanations is uninformed by the training of the ML model, which leads to misalignment between model predictions and explanations; and (ii) most of them rely on solving separate time-intensive optimization problems to find CF explanations for each input data point (which negatively impacts their runtime). This work makes a novel departure from the prevalent post-hoc paradigm (of generating CF explanations) by presenting CounterNet, an end-to-end learning framework which integrates predictive model training and the generation of counterfactual (CF) explanations into a single pipeline. Unlike post-hoc methods, CounterNet enables the optimization of the CF explanation generation only once together with the predictive model. We adopt a block-wise coordinate descent procedure which helps in effectively training CounterNet's network. Our extensive experiments on multiple real-world datasets show that CounterNet generates high-quality predictions, and consistently achieves 100% CF validity and low proximity scores (thereby achieving a well-balanced cost-invalidity trade-off) for any new input instance, and runs 3X faster than existing state-of-the-art baselines.

翻译：摘要：本文提出CounterNet，一种新型端到端学习框架，将机器学习模型训练与对应反事实解释的生成集成至单一端到端流程。反事实解释提供对比案例，即试图找到对实例特征值的最小修改，以使该实例上的机器学习模型预测变为预定义输出。现有反事实解释生成技术存在两大局限：(i) 所有方法均为为专有机器学习模型设计的后处理方法——因此其反事实解释生成过程未考虑模型训练信息，导致模型预测与解释之间的错位；(ii) 大多数方法需对每个输入数据点求解独立且耗时的优化问题来寻找反事实解释（这影响运行时效率）。本文通过提出CounterNet，突破了主流后处理反事实解释生成范式，这是一种将预测模型训练与反事实解释生成集成至单一流程的端到端学习框架。与后处理方法不同，CounterNet仅需与预测模型共同优化一次反事实解释生成过程。我们采用分块坐标下降法有效训练CounterNet网络。在多个真实世界数据集上的广泛实验表明，CounterNet生成高质量预测，对任意新输入实例实现100%反事实有效性与低邻近性分数（从而达成成本-无效性的良好平衡），且运行速度比现有最先进基线快3倍。