Generative models of observations under interventions have been a vibrant
topic of interest across machine learning and the sciences in recent years. For
example, in drug discovery, there is a need to model the effects of diverse
interventions on cells in order to characterize unknown biological mechanisms
of action. We propose the Sparse Additive Mechanism Shift Variational
Autoencoder, SAMS-VAE, to combine compositionality, disentanglement, and
interpretability for perturbation models. SAMS-VAE models the latent state of a
perturbed sample as the sum of a local latent variable capturing
sample-specific variation and sparse global variables of latent intervention
effects. Crucially, SAMS-VAE sparsifies these global latent variables for
individual perturbations to identify disentangled, perturbation-specific latent
subspaces that are flexibly composable. We evaluate SAMS-VAE both
quantitatively and qualitatively on a range of tasks using two popular single
cell sequencing datasets. In order to measure perturbation-specific
model-properties, we also introduce a framework for evaluation of perturbation
models based on average treatment effects with links to posterior predictive
checks. SAMS-VAE outperforms comparable models in terms of generalization
across in-distribution and out-of-distribution tasks, including a combinatorial
reasoning task under resource paucity, and yields interpretable latent
structures which correlate strongly to known biological mechanisms. Our results
suggest SAMS-VAE is an interesting addition to the modeling toolkit for machine
learning-driven scientific discovery.

生成模型，干预，稀疏加性机制迁移变分自动编码器，扰动模型，面向机器学习驱动的科学发现的利器。