Mechanisms Matter: Transportability of Cellular Perturbation Effects

Predicting cellular responses to perturbations across biological contexts is central to drug development, yet deep learning models often fail to outperform simple baselines. Leveraging causal transportability theory, we show that cross-context generalization is governed by shared causal mechanisms, not merely distributional similarity. To enable controlled evaluation, we develop a causal simulator that generates realistic semi-synthetic Perturb-seq datasets with tunable mechanistic divergence, providing benchmarks with known ground-truth causal structure. Further, we adapt the Vendi score to the perturbation setting to quantify effective response diversity and diagnose mode collapse, a failure mode invisible to standard per-perturbation metrics. Extensive experiments across six deep learning models and six simple baselines on semi-synthetic and real Perturb-seq datasets reveal a cross-context generalization gap: performance under cross-context splits drops substantially, often to simple baseline levels. Notably, even on synthetic data with fully specified causal structure, no model generalizes across contexts with different causal mechanisms. Across real and semi-synthetic datasets, lower cross-context concordance of transcriptome-wide impact tracks larger transfer gaps, providing an empirical proxy for perturbation-relevant mechanistic divergence.