Learning Deployable Causal Action Geometry under Temporal Non-Stationarity

Learning a response law for continuous decisions from observational time series is central to many real-world decision systems, yet temporal non-stationarity poses a fundamental challenge: outcome levels drift over time, causing naive models that fit raw outcomes to conflate treatment effects with shifting baseline conditions. Even when the one-step response is identifiable, unrestricted cross-time generalization remains fundamentally ill-posed. Thus, we introduce a constrained decision target based on a causal action geometry with anchored link-scale morphologies. Our approach constructs anchored contrasts over a family of link functions, isolating the action-dependent component of the response that can be shared across periods while allowing flexible, period-specific calibration. This yields a morphology-restricted oracle target for continuous decision making under temporal drift. Building on this formulation, we develop a two-stage orthogonal estimator that first obtains cross-fitted pilot estimates of anchored responses and then performs honest profiled selection over morphology and representation classes. The resulting estimator admits oracle guarantees for the restricted target and, under approximate persistence, produces decision rules that remain near optimal in future periods. Experiments on synthetic data and a large-scale e-commerce pricing application support the proposed target and its decision benefits under temporal shift.