Reinforcement learning (RL) algorithms often struggle to learn policies that
generalize to novel situations due to issues such as causal confusion,
overfitting to irrelevant factors, and failure to isolate control of state
factors. These issues stem from a common source: a failure to accurately
identify and exploit state-specific causal relationships in the environment.
While some prior works in RL aim to identify these relationships explicitly,
they rely on informal domain-specific heuristics such as spatial and temporal
proximity. Actual causality offers a principled and general framework for
determining the causes of particular events. However, existing definitions of
actual cause often attribute causality to a large number of events, even if
many of them rarely influence the outcome. Prior work on actual causality
proposes normality as a solution to this problem, but its existing
implementations are challenging to scale to complex and continuous-valued RL
environments. This paper introduces functional actual cause (FAC), a framework
that uses context-specific independencies in the environment to restrict the
set of actual causes. We additionally introduce Joint Optimization for Actual
Cause Inference (JACI), an algorithm that learns from observational data to
infer functional actual causes. We demonstrate empirically that FAC agrees with
known results on a suite of examples from the actual causality literature, and
JACI identifies actual causes with significantly higher accuracy than existing
heuristic methods in a set of complex, continuous-valued environments.

通过使用功能实际因果 (FAC) 和联合优化实际因果推断 (JACI) 算法，本研究在复杂连续数值环境中展示了 FAC 与实际因果文献中已知结果的一致性，并且 JACI 在识别实际原因的准确性上明显优于现有的启发式方法。

复杂环境中功能实际原因的自动发现

Automated Discovery of Functional Actual Causes in Complex Environments

Protocols for tasks such as authentication, electronic voting, and secure
multiparty computation ensure desirable security properties if agents follow
their prescribed programs. However, if some agents deviate from their
prescribed programs and a security property is violated, it is important to
hold agents accountable by determining which deviations actually caused the
violation. Motivated by these applications, we initiate a formal study of
program actions as actual causes. Specifically, we define in an interacting
program model what it means for a set of program actions to be an actual cause
of a violation. We present a sound technique for establishing program actions
as actual causes. We demonstrate the value of this formalism in two ways.
First, we prove that violations of a specific class of safety properties always
have an actual cause. Thus, our definition applies to relevant security
properties. Second, we provide a cause analysis of a representative protocol
designed to address weaknesses in the current public key certification
infrastructure.

本文研究了程序行为作为实际因果关系的形式化定义和建立了一种技术来确定程序行为作为实际原因，此方法可用于安全协议的各个方面，包括追溯协议违规的原因分析。

以程序行为为实际原因：追究责任的基石

Program Actions as Actual Causes: A Building Block for Accountability

We propose new definitions of (causal) explanation, using structural
equations to model counterfactuals. The definition is based on the notion of
actual cause, as defined and motivated in a companion paper. Essentially, an
explanation is a fact that is not known for certain but, if found to be true,
would constitute an actual cause of the fact to be explained, regardless of the
agent's initial uncertainty. We show that the definition handles well a number
of problematic examples from the literature.

利用结构方程来建模反事实，我们提出了（因果）解释的新定义。基于伴随论文中定义和推动的实际原因的概念，本文提出的解释是一个未知但如果被证明是真的，无论代理人最初的不确定性如何，都将构成被解释事实的实际原因。我们证明了该定义正确处理了文献中的一些问题示例。