Drug Discovery

When we pooled seven related anxiety-drug starting points into one workflow, the story stopped sounding like one lucky case study and started sounding like a reusable decision framework.

A mean effect can hide who should and should not receive an edit. That fail-closed boundary is part of the product, not a footnote.

Prediction can describe where a molecule sits. Causal reasoning helps decide which lever is worth trying next, what it may improve, and when the evidence is too weak…

A clean A/B test is already a causal design. The trouble starts when someone conditions on post-treatment behavior, slices the experiment the wrong way, or forgets what randomization…

Once you unroll time, an HMM is just an ordinary Bayesian network with repeated structure. That makes temporal reasoning much less mysterious and much more programmable.

Counterfactuals are where causal APIs stop describing populations and start answering the harder question: what would have happened for this exact case?

If observing X and setting X collapse to the same operation in your tooling, confounding has already beaten you.

A treatment can look worse in the aggregate and better in every subgroup. That is not a corner case. It is the reason your query surface needs association,…

Lead optimization does not stall because teams lack scores. It stalls because scores do not answer what analog to make next under competing objectives.