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：现有的因果推断（CI）模型主要局限于处理低维混杂因子和单一动作。我们提出了一种自回归（AR）CI框架，能够处理现代应用中常见的复杂混杂因子和序列动作。我们通过{\em序列化}实现这一目标，即将底层因果图的数据转化为一系列标记。这种方法不仅能够利用从任意有向无环图（DAG）生成的数据进行训练，还将现有CI能力扩展到能够使用{\em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高结果预测的准确性。我们证明，为CI调整的自回归模型在多种复杂应用中（例如迷宫导航、国际象棋残局博弈以及评估特定关键词对论文接收率的影响）均表现出高效性和有效性。