Average dose-response functions are widely used to summarize causal effects of continuous treatments, but most existing methods assume that the observed sample represents the target population. We study a covariate-shift setting in which covariates, treatment, and outcome are observed in a labelled source sample, while only covariates are observed in the target sample. We develop a two-sample local polynomial regression framework based on pseudo-outcomes that use source outcomes to address confounding and target covariates to define the population of interest. We further propose a source-to-target extension of distance covariance optimal weighting (DCOW), designed to remove treatment-covariate dependence in the source sample while aligning the weighted source covariate distribution with the target population. A central theoretical contribution is a weight-level analysis of this optimization-based procedure: we show that the population criterion identifies the oracle source-to-target weights and that approximate empirical minimizers, including exact minimizers as a special case, converge uniformly to these weights under regularity conditions. We also establish consistency and asymptotic normality of the resulting estimator. Simulations show that the proposed method improves target dose-response estimation relative to DCOW, generalized-propensity-score weighting, entropy balancing, and unweighted alternatives. We illustrate the method in a county-level analysis of PM2.5 exposure and subsequent heart-disease mortality using a source-target validation design.
Additive activation steering is calibrated in single-turn chat, then deployed inside agent scaffolds. The quantity usually reported for that move is a gain: a ratio of steered effects, T = Delta_agent / Delta_chat. We sweep eight family x arm dose-response cells over six models in both deployment contexts and show this ratio does not identify potency and efficacy. Reconstructing the published estimator in both of its forms on our own grids, its realized range contains 1 in five of five scorable cells, it moves with dose in four of five, and two cells with opposite potency shifts, both CI-clean on the primary grid, return gain intervals overlapping at a width under 0.08. Every scorable cell is an amplifier at one dose and an attenuator at another, so an amplify/attenuate taxonomy reports the dose it was read at. We replace the gain with a location: dEC50 = EC50_agent - EC50_chat, the cross-context difference in a curve location. It is signed both ways on this roster's primary grid, across four model families (+1.013 [+0.777, +1.273] against -12.368, -10.855, -5.497 and -886.066 elsewhere) and beats a vertical rescaling at equal complexity in all five cells of a frozen audit (four under the registered trim). Three deflationary accounts are measured and rejected on sign pattern and magnitude. We report the discipline at the same volume as the result: one cell is quarantined loudly, our pre-registered forecaster was refuted out of sample and is published as refuted, a registered salvage claim produced no qualifying cell and is reported unanswered, and a census of our own register reports registered branches no code here could have emitted. The consequence is a measurement instruction rather than a theorem: a transfer conclusion read at one strength does not identify what changed, because a displacement and a gain are not distinguishable from a single operating point.
Modelling psychological disorders in artificial agents offers a testbed for computational psychiatry and a lens on affective-control failure modes. Prior work induces one or two disorders by hand-tuned reward shaping, labels the behaviour post hoc, and reports single runs. We recast disorder modelling as dose-controllable manipulation of cognitive appraisal signals in an appraisal-guided PPO agent, expressing seven disorders (anxiety, mania, obsessive-compulsive checking, depression, impulsivity, addiction, and post-traumatic stress) each as a single knob grounded in a computational psychiatry account, with each symptom measured by a preregistered assay. Across more than a thousand runs (10 seeds, four controls, 95% confidence intervals) every disorder shows a graded, monotone dose-response that no control reproduces. Beyond these induced effects, three findings emerge that were not written into the reward: disorders self-organise into a two-dimensional affective space in which mania mirrors anxiety; removing a knob remits reward-distortion disorders (mania, checking, addiction) but not avoidance disorders (anxiety, PTSD), which recover under a graded exposure curriculum; and two simultaneous knobs interact nonadditively, yielding testable comorbidity predictions. The depression and addiction knobs further reproduce their double dissociation in a 3D pixel environment (MiniWorld) with a standard convolutional agent and no appraisal critic, showing the framework generalises beyond grid worlds.