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

arXiv: https://arxiv.org/abs/2101.05136 · HTML: https://mlsanity.com/paper/2101.05136/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 66 references that are in the map.
- First posted on arXiv 13 Jan 2021.

## Builds on (most cited first, 7 of 7)

- [Pyro: Deep Universal Probabilistic Programming](https://mlsanity.com/paper/1810.09538/) (2018)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [Stochastic Backpropagation and Approximate Inference in Deep Generative Models](https://mlsanity.com/paper/1401.4082/) (2014)
- [Full Law Identification In Graphical Models Of Missing Data: Completeness Results](https://mlsanity.com/paper/2004.04872/) (2020)
- [Identifying Causal Effects with the R Package causaleffect](https://mlsanity.com/paper/1806.07161/) (2018)
- [Identification and Estimation of Causal Effects from Dependent Data](https://mlsanity.com/paper/1902.01443/) (2019)
- [Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems](https://mlsanity.com/paper/1911.02175/) (2019)

## Built on by (most cited first, 3 of 3)

- [Nanopublication-Based Semantic Publishing and Reviewing: A Field Study with Formalization Papers](https://mlsanity.com/paper/2203.01608/) (2022)
- [When Counterfactual Reasoning Fails: Chaos and Real-World Complexity](https://mlsanity.com/paper/2503.23820/) (2025)
- [Do-calculus enables estimation of causal effects in partially observed biomolecular pathways](https://mlsanity.com/paper/2102.06626/) (2021)

## Abstract (arXiv)

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.