Active Learning for Optimal Intervention Design in Causal Models

by   Jiaqi Zhang, et al.

An important problem across disciplines is the discovery of interventions that produce a desired outcome. When the space of possible interventions is large, making an exhaustive search infeasible, experimental design strategies are needed. In this context, encoding the causal relationships between the variables, and thus the effect of interventions on the system, is critical in order to identify desirable interventions efficiently. We develop an iterative causal method to identify optimal interventions, as measured by the discrepancy between the post-interventional mean of the distribution and a desired target mean. We formulate an active learning strategy that uses the samples obtained so far from different interventions to update the belief about the underlying causal model, as well as to identify samples that are most informative about optimal interventions and thus should be acquired in the next batch. The approach employs a Bayesian update for the causal model and prioritizes interventions using a carefully designed, causally informed acquisition function. This acquisition function is evaluated in closed form, allowing for efficient optimization. The resulting algorithms are theoretically grounded with information-theoretic bounds and provable consistency results. We illustrate the method on both synthetic data and real-world biological data, namely gene expression data from Perturb-CITE-seq experiments, to identify optimal perturbations that induce a specific cell state transition; the proposed causal approach is observed to achieve better sample efficiency compared to several baselines. In both cases we observe that the causally informed acquisition function notably outperforms existing criteria allowing for optimal intervention design with significantly less experiments.


page 1

page 2

page 3

page 4


Matching a Desired Causal State via Shift Interventions

Transforming a causal system from a given initial state to a desired tar...

Interventions, Where and How? Experimental Design for Causal Models at Scale

Causal discovery from observational and interventional data is challengi...

Trust Your ∇: Gradient-based Intervention Targeting for Causal Discovery

Inferring causal structure from data is a challenging task of fundamenta...

Differentiable Multi-Target Causal Bayesian Experimental Design

We introduce a gradient-based approach for the problem of Bayesian optim...

From Intervention to Domain Transportation: A Novel Perspective to Optimize Recommendation

The interventional nature of recommendation has attracted increasing att...

Active Learning of Causal Structures with Deep Reinforcement Learning

We study the problem of experiment design to learn causal structures fro...

Causal Geometry

Information geometry has offered a way to formally study the efficacy of...

Code Repositories


Active learning for optimal intervention design in causal models

view repo

Please sign up or login with your details

Forgot password? Click here to reset