ICML 2025

Identifying biological perturbation targets through
causal differential networks
Menghua Wu 1 Umesh Padia 1 Sean H. Murphy 1 Regina Barzilay 1 Tommi Jaakkola 1
Abstract
Identifying variables responsible for changes to a
biological system enables applications in drug tar-
get discovery and cell engineering. Given a pair
of observational and interventional datasets, the
goal is to isolate the subset of observed variables
that were the targets of the intervention. Directly
applying causal discovery algorithms is challeng-
ing: the data may contain thousands of variables
with as few as tens of samples per intervention,
and biological systems do not adhere to classical
causality assumptions. We propose a causality-
inspired approach to address this practical setting.
First, we infer noisy causal graphs from the obser-
vational and interventional data. Then, we learn to
map the differences between these graphs, along
with additional statistical features, to sets of vari-
ables that were intervened upon. Both modules
are jointly trained in a supervised framework, on
simulated and real data that reflect the nature of bi-
ological interventions. This approach consistently
outperforms baselines for perturbation modeling
on seven single-cell transcriptomics datasets. We
also demonstrate significant improvements over
current causal discovery methods for predicting
soft and hard intervention targets across a variety
of synthetic data.1
1. Introduction
Cells form the basis of biological systems, and they take on
a multitude of dynamical states throughout their lifetime.
In addition to natural factors like cell cycle, external per-
turbations (e.g. drugs, gene knockdown) can alter a cell’s
state. While perturbations can affect numerous downstream
1Department of Computer Science, Massachusetts Institute of
Technology, Cambridge, MA, USA. Correspondence to: Menghua
Wu <rmwu@mit.edu>.
Proceedings of the 42 nd International Conference on Machine
Learning, Vancouver, Canada. PMLR 267, ...