Planning effective interventions in biological systems requires treatment-effect models that adapt to unseen biological contexts by identifying their specific underlying mechanisms. Yet single-cell perturbation datasets span only a handful of biological contexts, and existing methods cannot leverage new interventional evidence at inference time to adapt beyond their training data. To meta-learn a perturbation effect estimator, we present MapPFN, a prior-data fitted network (PFN) pre-trained on synthetic data generated from a prior over causal perturbations. Given a set of experiments, MapPFN uses in-context learning to predict post-perturbation distributions. Pre-trained on in silico gene knockouts alone, MapPFN identifies differentially expressed genes on par with models trained on real single-cell data. Fine-tuned, it consistently outperforms baselines across downstream datasets. Our code, model and data are available at https://marvinsxtr.github.io/MapPFN.

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