Kapnova uses econometrics, forecasting, simulation and optimization to model business decisions. Its core research advantage is causal identification: determining whether a lever actually changed an outcome or simply moved alongside it.
That capability is rooted in published MIT doctoral research and adapted here from survival analysis to real business decisions.
Each method answers a different part of the question. Causal identification determines when the evidence supports saying a decision actually caused the outcome.
The methods work together. Identification is what lets a forecast or an optimization be stated as a claim about what a decision will cause, rather than what has tended to move alongside it, which is the difference between a number that describes the past and one you can act on.
It develops causal methods for the highest-stakes setting there is, whether a drug causes a change in cancer or survival outcomes, estimated from observational data. Kapnova is built on years of this doctoral research.
These are the methods Kapnova uses to build the causal models behind every answer. They are how the engine works out what actually drove an outcome, and what would happen if you changed it. Each one below names the chapter it comes from and the decision it answers.
The thesis reconstructs a randomized trial it cannot run, whether a diabetes drug prevents cancer, from observational data alone. Your company cannot run a controlled experiment on an 8% price move either, so the engine emulates that trial from your own history. This is the named, published version of “what will this cause,” and it is why the whole approach is more than a forecast.
Estimators built to stay consistent even when the underlying models are imperfect. This is why an effect the engine reports is defensible rather than fragile, and why the number survives a finance review.
A real effect even when the treated and untreated groups barely compare. That is the loyalty self-selection problem exactly, where members and non-members are not alike to begin with.
Modeling mutually exclusive outcomes so one cannot mask another, and splitting an effect into its direct and mediated paths. These are the flat-number-hides-a-loss case and the price-to-sentiment-to-demand case.
The effect of a decision, and for whom, not just on average.
The thesis uses foundation models to screen many drug-disease pairs for causal signal. The agents do the same for decisions, generating and ranking hypotheses by estimated causal effect, which grounds the agentic argument in published work, not aspiration.
The target trial emulation the causal engine runs on. It compares two diabetes therapies across 93,353 patient records from a UK primary-care database, and it faces the two problems every business decision faces: poor overlap between the groups being compared, and a competing risk that can mask the outcome. The answer arrives as an interval, not a point.
A scaling law for how much model capacity implicit reasoning actually needs, accepted at one of the three top-tier machine learning conferences alongside NeurIPS and ICLR. This is the machine learning research behind the agents that build the causal maps, rather than the causal methods themselves.
Shenbo Xu’s peer-reviewed and forthcoming work in causal inference and machine learning. The methods Kapnova runs are drawn from these papers and from the dissertation above.
The thesis is built on the counterfactual, what would have happened under the other choice, the exact quantity a dashboard never contains and the one the engine estimates. The techniques that make that estimate trustworthy in medicine are the ones running under every Kapnova decision.
The author's own research page frames these methods as cross-domain, listing sales and marketing among the applications.
The platform applies methods from this research and the broader causal-inference field. It is not itself a peer-reviewed artifact, and the thesis validated the methods in medicine, not business.
Start with your URL and Kapnova will surface what it can find from public data alone. Then connect your own data to measure what past decisions actually caused and where the next revenue or profit opportunity may be.