Leveraging Structured Biological Knowledge for Counterfactual Inference: a Case Study of Viral Pathogenesis

TL;DR

This paper introduces a method to convert structured biological causal knowledge into quantitative models for counterfactual inference, demonstrated through case studies in viral pathogenesis and SARS-CoV-2 research.

Contribution

It presents a novel approach to leverage qualitative biological knowledge graphs for causal inference, reducing the need for extensive domain expertise in model specification.

Findings

Accurate counterfactual inference in systems biology.

Versatile application to SARS-CoV-2 cytokine storm analysis.

Feasibility of converting qualitative knowledge into quantitative models.

Abstract

Counterfactual inference is a useful tool for comparing outcomes of interventions on complex systems. It requires us to represent the system in form of a structural causal model, complete with a causal diagram, probabilistic assumptions on exogenous variables, and functional assignments. Specifying such models can be extremely difficult in practice. The process requires substantial domain expertise, and does not scale easily to large systems, multiple systems, or novel system modifications. At the same time, many application domains, such as molecular biology, are rich in structured causal knowledge that is qualitative in nature. This manuscript proposes a general approach for querying a causal biological knowledge graph, and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question. We demonstrate the feasibility, accuracy and versatility of this approach using two case studies in systems biology. The first demonstrates the appropriateness of the underlying assumptions and the accuracy of the results. The second demonstrates the versatility of the approach by querying a knowledge base for the molecular determinants of a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced cytokine storm, and performing counterfactual inference to estimate the causal effect of medical countermeasures for severely ill patients.