论文标题
合成与程序合成的算法追索的可解释的反事实策略
Synthesizing explainable counterfactual policies for algorithmic recourse with program synthesis
论文作者
论文摘要
能够提供反事实干预措施 - 我们必须采取的行动序列才能发生理想的结果 - 至关重要的是要解释如何通过黑盒机器学习模型改变不利的决定（例如，拒绝贷款请求）。现有的解决方案主要集中于生成可行的干预措施，而无需提供有关其理由的解释。此外，他们需要为每个用户解决一个单独的优化问题。在本文中，我们采用另一种方法，并学习一个程序，该程序输出了一系列可解释的反事实动作，给定用户描述和因果图。我们利用程序合成技术，加强学习，再加上蒙特卡洛树搜索有效的探索，并学习学习为每种建议的动作提取解释。对合成和现实世界数据集的实验评估表明，我们的方法是如何通过对现有解决方案的黑盒分类器的查询较少来产生有效的干预措施，并提供了与可解释的解释相辅相成的其他好处。
Being able to provide counterfactual interventions - sequences of actions we would have had to take for a desirable outcome to happen - is essential to explain how to change an unfavourable decision by a black-box machine learning model (e.g., being denied a loan request). Existing solutions have mainly focused on generating feasible interventions without providing explanations on their rationale. Moreover, they need to solve a separate optimization problem for each user. In this paper, we take a different approach and learn a program that outputs a sequence of explainable counterfactual actions given a user description and a causal graph. We leverage program synthesis techniques, reinforcement learning coupled with Monte Carlo Tree Search for efficient exploration, and rule learning to extract explanations for each recommended action. An experimental evaluation on synthetic and real-world datasets shows how our approach generates effective interventions by making orders of magnitude fewer queries to the black-box classifier with respect to existing solutions, with the additional benefit of complementing them with interpretable explanations.
