Causal discovery has become a vital tool for scientists and practitioners
wanting to discover causal relationships from observational data. While most
previous approaches to causal discovery have implicitly assumed that no expert
domain knowledge is available, practitioners can often provide such domain
knowledge from prior experience. Recent work has incorporated domain knowledge
into constraint-based causal discovery. The majority of such constraint-based
methods, however, assume causal faithfulness, which has been shown to be
frequently violated in practice. Consequently, there has been renewed attention
towards exact-search score-based causal discovery methods, which do not assume
causal faithfulness, such as A*-based methods. However, there has been no
consideration of these methods in the context of domain knowledge. In this
work, we focus on efficiently integrating several types of domain knowledge
into A*-based causal discovery. In doing so, we discuss and explain how domain
knowledge can reduce the graph search space and then provide an analysis of the
potential computational gains. We support these findings with experiments on
synthetic and real data, showing that even small amounts of domain knowledge
can dramatically speed up A*-based causal discovery and improve its performance
and practicality.

本篇论文探讨如何在基于 A * 方法的因果关系发现中高效地整合多种类型的专家领域知识以减少图形搜索空间，并提供了潜在的计算增益分析。作者通过合成数据和真实数据的实验支持这些发现，表明即使少量领域知识都可以极大地提高 A * 方法的效率和实用性。

A * 算法因果发现中的领域知识

Domain Knowledge in A*-Based Causal Discovery

We present a new class of structured reinforcement learning
policy-architectures, Implicit Two-Tower (ITT) policies, where the actions are
chosen based on the attention scores of their learnable latent representations
with those of the input states. By explicitly disentangling action from state
processing in the policy stack, we achieve two main goals: substantial
computational gains and better performance. Our architectures are compatible
with both: discrete and continuous action spaces. By conducting tests on 15
environments from OpenAI Gym and DeepMind Control Suite, we show that
ITT-architectures are particularly suited for blackbox/evolutionary
optimization and the corresponding policy training algorithms outperform their
vanilla unstructured implicit counterparts as well as commonly used explicit
policies. We complement our analysis by showing how techniques such as hashing
and lazy tower updates, critically relying on the two-tower structure of ITTs,
can be applied to obtain additional computational improvements.

该论文提出了一种新的结构强化学习策略架构，Implicit Two-Tower（ITT）策略，并在 OpenAI Gym 和 DeepMind Control Suite 的 15 个环境上进行了测试，表明 ITT-architectures 特别适用于黑盒 / 进化优化，并且相应的策略训练算法优于其未经结构化改进的隐式对照组以及常用的显式策略。