Existing causal inference (CI) models are limited to primarily handling low-dimensional confounders and singleton actions. We propose an autoregressive (AR) CI framework capable of handling complex confounders and sequential actions common in modern applications. We accomplish this by {\em sequencification}, transforming data from an underlying causal diagram into a sequence of tokens. This approach not only enables training with data generated from any DAG but also extends existing CI capabilities to accommodate estimating several statistical quantities using a {\em single} model. We can directly predict interventional probabilities, simplifying inference and enhancing outcome prediction accuracy. We demonstrate that an AR model adapted for CI is efficient and effective in various complex applications such as navigating mazes, playing chess endgames, and evaluating the impact of certain keywords on paper acceptance rates.

翻译：现有因果推断模型主要局限于处理低维混杂因子和单一干预动作。我们提出一种能够处理现代应用中常见的复杂混杂因子和序列化干预动作的自回归因果推断框架。我们通过"序列化"技术实现这一目标，即将基础因果图数据转化为符号序列。该方法不仅支持使用任意有向无环图生成的数据进行训练，还将现有因果推断能力扩展到能够通过"单一"模型估计多种统计量。我们可以直接预测干预概率，从而简化推断过程并提升结果预测精度。实验表明，为因果推断适配的自回归模型在多种复杂应用中均表现出高效性和有效性，例如迷宫导航、国际象棋残局推演，以及评估特定关键词对论文录用率的影响。