by-causal-reasoning

To generate evidence-grounded hypotheses for protein design failures, map your discriminating diagnostic features to a knowledge graph. This translates structural context into ranked, canonical mechanisms with traceable rationale and falsifiable predictions.

Causal reasoning for failure mechanisms maps diagnostic features to canonical mechanisms using a knowledge graph. It provides auditable explanations by enforcing evidence grounding, assigning confidence via a precedence table, and outputting up to five ranked hypotheses.

You can use a knowledge graph to create falsifiable predictions by running a script with your diagnosis data and knowledge graph inputs. The workflow outputs structured hypotheses, each containing supporting evidence, contradicting evidence, and a specific prediction.

Yes, structured diagnostics are required to identify underlying failure mechanisms. Optional structural context can be added to the diagnostics to further anchor the generated mechanistic hypotheses and improve the traceable rationale.

Downstream guidance depends on the assigned confidence and evidence mix of the generated hypotheses. It directs your campaigns toward specific next steps, including optimization, hypothesis debate, or knowledge-store updates.

Yes, you can obtain auditable explanations for design failures by anchoring diagnostic features in a knowledge graph. The system automatically cites supporting and contradicting evidence per hypothesis, ensuring traceable rationale without manual tracing.