用自回归模型处理复杂因果推断，支持序列动作与多重因果查询。
Deep Autoregressive Models as Causal Inference Engines
- 将因果图转为序列令牌，统一建模复杂场景下的因果关系。
- 单模型可同时估计多种因果效应，干预后概率计算更高效准确。
- 适用于迷宫导航、棋局决策等复杂任务，超越传统强化学习框架。
现有因果推断（CI）模型通常受限于低维混杂因子和单一动作。本文提出一种自回归（AR）因果推断框架，能够处理现代应用中常见的复杂混杂因子和序列动作。该方法通过「序列化」（sequencification）将底层因果图转化为一系列令牌，不仅支持从一大类有向无环图（DAGs）生成的数据训练，还能用单个模型估计多种因果量。可以直接从干预分布中计算概率，简化推断过程并提升结果预测准确性。实验表明，适配后的自回归模型在迷宫导航、国际象棋残局、关键词对论文录用率影响评估等复杂场景中均表现高效有效，涵盖传统强化学习之外的因果问题。
原文摘要 · Abstract (English)
Existing causal inference (CI) models are often restricted to data with low-dimensional confounders and singleton actions. We propose an autoregressive (AR) CI framework capable of handling complex confounders and sequential actions commonly found in modern applications. Our approach accomplishes this using {\em sequencification}, which transforms data from an underlying causal diagram into a sequence of tokens. Sequencification not only accommodates training with data generated from a large class of DAGs, but also extends existing CI capabilities to estimate multiple causal quantities using a {\em single} model. We can directly compute probabilities from interventional distributions, simplifying inference and improving 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, where we consider causal queries beyond standard reinforcement learning-type questions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。