Representative work
These examples show the kind of problems Causality Graphs is built to support: pharmacological evidence under imperfect controls, temporal complexity, or interpretation risk.
How projects are framed
Constraint: A promising response pattern was difficult to interpret without a concurrent control.
Method: DAG refinement plus explicit counterfactual framing around likely confounding and selection processes.
Impact: The study team gained a clearer interpretation boundary and a more credible next-evidence strategy.
Constraint: Dose changes, dropouts, and symptom dynamics blurred the treatment story over time.
Method: Dynamic causal modeling to separate temporal structure, pathway timing, and evolving response states.
Impact: The resulting model supported better reasoning about progression, timing, and endpoint relevance.
Constraint: Operational realities introduced expectation effects and outcome interpretation risk.
Method: Structured causal assumptions, mediation review, and sensitivity framing for interpretation robustness.
Impact: Leadership received a cleaner account of what could be claimed and what required caution.
Typical problem types
Single-arm pharmacological studies that need stronger interpretation boundaries.
Longitudinal treatment response where dose changes, timing, or adaptation matter.
Confounding-heavy observational evidence that needs explicit causal assumptions.
Incomplete blinding or operational constraints that affect how outcomes should be read.