Automated Discovery of Pairwise Interactions from Unstructured Data

Zuheng Xu, Moksh Jain, Alisandra Kaye Denton, Shawn T. Whitfield, Aniket Rajiv Didolkar, Berton Earnshaw, Jason Hartford

Keywords: causal independence testing, representation learning, active learning

Abstract:

Pairwise interactions between perturbations to a system can provide evidence for the causal dependencies of the underlying underlying mechanisms of a system. When observations are low dimensional, hand crafted measurements, detecting interactions amounts to simple statistical tests, but it is not obvious how to detect interactions between perturbations affecting latent variables.
We derive two interaction tests that are based on pairwise interventions, and show how these tests can be integrated into an active learning pipeline to efficiently discover pairwise interactions between perturbations.
We illustrate the value of these tests in the context of biology, where
pairwise perturbation experiments are frequently used to reveal interactions
that are not observable from any single perturbation.
Our tests can be run on unstructured data, such as
the pixels in an image, which enables a more general notion of interaction than
typical cell viability experiments, and can be run on cheaper experimental assays.
We validate on several synthetic and real biological experiments that our tests are able to identify interacting pairs effectively.
We evaluate our approach on a real biological experiment where we knocked out
50 pairs of genes and measured the effect with microscopy images. We show that
we are able to recover significantly more known biological interactions than
random search and standard active learning baselines.

Primary Area: causal reasoning

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Submission Number: 7425