In the fundamental statistics course, students are taught to remember the
well-known saying: "Correlation is not Causation". Till now, statistics (i.e.,
correlation) have developed various successful frameworks, such as Transformer
and Pre-training large-scale models, which have stacked multiple parallel
self-attention blocks to imitate a wide range of tasks. However, in the
causation community, how to build an integrated causal framework still remains
an untouched domain despite its excellent intervention capabilities. In this
paper, we propose the Causal Graph Routing (CGR) framework, an integrated
causal scheme relying entirely on the intervention mechanisms to reveal the
cause-effect forces hidden in data. Specifically, CGR is composed of a stack of
causal layers. Each layer includes a set of parallel deconfounding blocks from
different causal graphs. We combine these blocks via the concept of the
proposed sufficient cause, which allows the model to dynamically select the
suitable deconfounding methods in each layer. CGR is implemented as the stacked
networks, integrating no confounder, back-door adjustment, front-door
adjustment, and probability of sufficient cause. We evaluate this framework on
two classical tasks of CV and NLP. Experiments show CGR can surpass the current
state-of-the-art methods on both Visual Question Answer and Long Document
Classification tasks. In particular, CGR has great potential in building the
"causal" pre-training large-scale model that effectively generalizes to diverse
tasks. It will improve the machines' comprehension of causal relationships
within a broader semantic space.

本文提出了 Causal Graph Routing（CGR）框架，通过干预机制揭示数据中隐藏的因果关系，并在计算机视觉和自然语言处理领域的任务中超过当前最先进方法，具有建立具有因果性的预训练大规模模型的潜力，以在更广泛的语义空间内提高机器对因果关系的理解能力。