Evidence-Type Competition: When Can Interventional Data Teach Language Models Causal Direction?
Interventional data is widely regarded as the gold standard for teaching models causal reasoning. We test this assumption in a fully controlled synthetic environment pitting observational correlation against causal effect, and find it fails instructively. In Simpson's-paradox worlds, where the two have systematically opposite signs, increasing the fraction of interventional samples in pretraining does not improve causal direction: the magnitude of the model's do()-response grows monotonically, yet its sign is copied from the observational context. What governs whether interventional evidence is used is not the training mixture but the evidence type present in the context at inference time...
| Authors | Xining Xun |
|---|---|
| Primary category | cs.CL |
| Published | 31 July 2026 |
| Last updated at source | 31 July 2026 |
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