  For text-based AI systems to interact in the real world, causal reasoning is
an essential skill. Since interventional data is costly to generate, we study
to what extent an agent can learn causal reasoning from passive data.
Specifically, we consider an axiomatic training setup where an agent learns
from multiple demonstrations of a causal axiom (or rule), rather than
incorporating the axiom as an inductive bias or inferring it from data values.
A key question is whether the agent would learn to generalize from the axiom
demonstrations to new scenarios. For example, if a transformer model is trained
on demonstrations of the causal transitivity axiom over small graphs, would it
generalize to applying the transitivity axiom over large graphs? Our results,
based on a novel axiomatic training scheme, indicate that such generalization
is possible. We consider the task of inferring whether a variable causes
another variable, given a causal graph structure. We find that a 67 million
parameter transformer model, when trained on linear causal chains (along with
some noisy variations) can generalize well to new kinds of graphs, including
longer causal chains, causal chains with reversed order, and graphs with
branching; even when it is not explicitly trained for such settings. Our model
performs at par (or even better) than many larger language models such as
GPT-4, Gemini Pro, and Phi-3. Overall, our axiomatic training framework
provides a new paradigm of learning causal reasoning from passive data that can
be used to learn arbitrary axioms, as long as sufficient demonstrations can be
generated.
