counterfactual explanation
Definition
A counterfactual explanation is an interpretable method for understanding causal relationships by identifying the minimal changes to input variables that would alter an outcome. In biological networks, it answers questions like 'what minimal perturbations to gene expression or protein interactions would change a cellular phenotype?' Unlike correlational approaches, counterfactual explanations reveal actionable interventions by showing which specific nodes or edges, if modified, would produce a different biological state. This approach is crucial for drug target identification, understanding disease mechanisms, and predicting therapeutic interventions, as it identifies the smallest set of molecular changes needed to achieve a desired outcome.
Visualize counterfactual explanation in Nodes Bio
Researchers can use Nodes Bio to visualize counterfactual pathways by highlighting alternative network configurations that lead to different biological outcomes. By comparing the actual network state with counterfactual scenarios, users can identify critical nodes (genes, proteins) whose modification would alter disease progression or drug response. Interactive network visualization enables exploration of multiple counterfactual scenarios simultaneously, revealing potential therapeutic targets and intervention points.
Visualization Ideas:
- Side-by-side comparison of actual vs. counterfactual gene regulatory networks with modified nodes highlighted
- Differential pathway maps showing edge weight changes required to achieve alternative phenotypes
- Interactive perturbation networks displaying cascading effects of counterfactual interventions on downstream targets
Example Use Case
A cancer researcher investigates why certain tumors resist immunotherapy. Using single-cell RNA-seq data, they build a gene regulatory network of immune checkpoint pathways. Counterfactual analysis reveals that if three specific transcription factors were upregulated (the counterfactual scenario), resistant tumors would exhibit the same molecular signature as responsive ones. This identifies potential combination therapy targets. The researcher visualizes both actual and counterfactual network states to understand which regulatory edges must be modified to restore treatment sensitivity.