Method stack
This page explains the methodological toolkit behind the consultancy. The emphasis is not methodological theater, but disciplined reasoning that supports real evidence decisions.
DAGs give teams a compact way to express assumptions about exposures, outcomes, confounders, mediators, and selection processes before statistical habits take over.
Clarify which variables should be adjusted for and which should not.
Expose hidden sources of bias in complex pharmacological evidence settings.
Support cross-functional alignment between clinical, stats, and strategy teams.
When biology and treatment effects evolve over time, a static snapshot can mislead. Dynamic causal models help represent timing, feedback, and changing states.
Useful for longitudinal treatment response and adaptation effects.
Bring temporal structure into the interpretation rather than treating it as noise.
Help reason about interventions within evolving systems.
Some variables are both consequences of prior treatment and determinants of future treatment or outcomes. These settings need more care than routine adjustment.
Separate evolving confounding from causal pathways.
Avoid naive adjustments that distort the estimand.
Improve interpretation for sequential treatment settings.
Understanding whether an effect travels through a mechanistic pathway, an operational artifact, or a measurement process often changes the scientific story.
Distinguish direct and indirect effects where it matters.
Support biomarker and mechanism-driven interpretation.
Reveal when a pathway assumption is doing too much work.
Good consulting does not stop at a single preferred model. It shows how conclusions move when assumptions weaken or alternative structures are considered.
Stress-test claims under limited evidence quality.
Make uncertainty visible without collapsing into indecision.
Help stakeholders understand the shape of the risk, not just its existence.
When ideal trial conditions are unavailable, multiple imperfect sources often have to be brought into one disciplined reasoning frame.
Link different evidence fragments through explicit structure.
Support decision-making when studies are non-ideal or partially comparable.
Turn fragmented evidence into a coherent strategic narrative.