Counterfactual Feature Interventions for Robust Tabular Reasoning in Small Language Models

Small language models are increasingly used as lightweight reasoners over structured records, but their behavior under tabular distribution shift is poorly characterized. This paper evaluates whether counterfactual feature interventions improve robustness when rows are verbalized as reasoning examples. We construct an oracle synthetic benchmark of mixed-type tabular rules with threshold, conjunction, disjunction, exception, and interaction families; generate matched no-augmentation, random-row augmentation, nuisance-counterfactual, label-preserving perturbation, and decisive counterfactual-intervention regimes; and evaluate 625 method, shift, rule, and seed cells. Decisive counterfactual intervention augmentation improves mean out-of-distribution accuracy by 21.3 percentage points over no augmentation and 17.0 points over random-row augmentation, with bootstrap intervals above zero. It also improves paired counterfactual consistency by 35.5 and 29.4 points over the same baselines. The original success criterion is only partly met because in-distribution accuracy retention is -2.3 points relative to the no-augmentation baseline, exceeding the pre-specified tolerance. The evidence supports counterfactual interventions as a useful robustness stressor and training signal in oracle tabular rule tasks, while limiting claims about real data until intervention-feasibility and shift manifests are available.