因果基礎模型

摘要

因果推論是從資料中估計處理或介入效應的實踐。傳統上，每個新問題都需要一套量身打造的流程：先提出因果機制，選擇相容的估計器，再加以訓練。與此同時，機器學習在各種不同情境與模態中，有很大一部分已轉向基礎模型的範式：以大規模預訓練一次網路，再無需微調即可應用於新任務。因果基礎模型（Causal Foundation Models, CFMs）將此範式帶入因果推論。CFMs 是預訓練神經網路，能在全新的資料集上利用上下文學習估計因果量（如平均處理效應），且無需更新模型。本文提供這個新興領域的實用導論；我們會在討論 CFMs 之前，先概述因果推論與機器學習的必要背景。全文包含範例程式碼與 Jupyter notebooks。

English

Causal inference is the practice of estimating the effect of a treatment or intervention from data. It traditionally requires a bespoke pipeline for every new problem: first proposing a causal mechanism, selecting a compatible estimator, and finally training it. Meanwhile, across diverse settings and modalities, much of machine learning has shifted to the paradigm of foundation models: networks pretrained once at scale and applied to new tasks without fine-tuning. Causal foundation models (CFMs) bring this paradigm to causal inference. CFMs are pretrained neural networks that estimate causal quantities, such as the average treatment effect, on entirely new datasets using in-context learning without requiring model updates. This work provides a practical introduction to this emerging area. We summarize the necessary background in causal inference and machine learning before discussing CFMs. Throughout, we include example code and Jupyter notebooks.