LOCA-RCA: A Label-Free Counterfactual Benchmark for Dependence on Cited Evidence in RCA Agents

LLM agents increasingly produce root-cause diagnoses accompanied by citations to observability telemetry, yet nothing checks whether the diagnosis actually depends on the evidence it cites. We introduce LOCA-RCA (Label-free Observability Citation Ablation for Root Cause Analysis), a training-free and label-free benchmark that removes exactly the evidence an agent itself cited, re-runs it, and compares the outcome against removing an equal number of non-cited items. Across 7 models and 3 deployments (335 OpenRCA incidents, of which Market and Bank are two schemas of one testbed), citations are counterfactually load-bearing well beyond a size-matched removal of evidence the agent did not cite, for most models, and the dependence runs through two channels with different operational meanings: switching to a different root cause (flip) or refusing to answer (abstain). Reporting them separately reverses the naive ranking. Whole-pool citation makes specificity structurally unmeasurable rather than low, which we report as a benchmark output rather than a missing row; a $k=3$ citation budget removes the behaviour on the one deployment and two models where we tested it. A salience-matched control leaves the contrast intact in 7 of 9 model-deployment cells. Item-level human reliability is established under none of four annotation rounds, so the metric stands on its construction; and base accuracy against gold is 0-11% on two deployments, so this is an audit signal about grounding, not a filter for right answers. We release the construction scripts, probes, metrics, per-incident traces and annotation material.