What Do Activation Interventions Establish? Causal Controls for Autoregressive Time-Series Foundation Models

Time-series foundation models (TSFMs) are increasingly studied through interventions on their internal activations; such interventions are also proposed as practical tools, for example to steer a forecast toward a historical crash. When an intervention succeeds, what has it established? Success is usually measured by restoration, the fraction of the gap to a donor’s forecast that the edit closes, which shows only that the edited quantity can influence the forecast. It does not show that the effect continues after the edit stops, that the edited site is special, what carries the effect forward, or that the displayed change comes from the edit. We identify these four causal claims and introduce, to our knowledge, the first causal controls for them in autoregressive TSFMs: releasing the edit and scoring later steps, matched edits at other sites, replaying the emitted values in an unedited run and simplifying them, and an activation-by-scale factorial; two of them apply to any TSFM. With them, each claim can be tested rather than assumed. On Chronos-T5, each control changes what a successful intervention can be taken to show. On observed traffic and pedestrian series, an edit that removes 82% of the gap while active removes only 7% once it stops. On controlled synthetic series, where a four-step edit does persist (96%), the effect is carried by the emitted forecast values: collapsing them to their mean retains essentially all of the shape improvement, yet with a key–value cache, replay recovers only about 70% of an early-layer edit’s effect. In the six public Chronos cases of time2time, which reports steering forecasts between market regimes, a case-specific output rescaling produces the displayed direction in all six, while the activation transplant alone moves the median forecast the opposite way in five (three with intervals excluding zero), although it shifts tails and drawdowns toward the donor regime. A successful intervention establishes influence; each stronger causal claim requires the causal control that tests it.