Working with autoresearch

Autoresearch answers one standing question per model: who in a population will do a target within a horizon? For example, "which active users will open a report in the next 7 days?".

1. The user names a target (an event or a PostHog action), a population and a horizon in days.
2. A training agent runs in a PostHog sandbox. It writes feature SQL and a model, scores each experiment on a holdout set, keeps what improves holdout AUC and discards the rest.
3. The server picks the champion from the kept experiments. The agent proposes; the backend decides.
4. Every cadence (daily by default, at 02:00 UTC), the champion scores the inference population. Each scored person gets an autoresearch_prediction event with their probability. When the model's write_person_property is on, a person who resolves to a current distinct ID also gets the model's output person property, for example predicted_p_adopt_insights_14d. It is off by default for new models. Turn it on with autoresearch-partial-update before you target people by score in a flag, cohort, workflow or insight. Turning it off removes the property from the people the model scored. Changing the setting needs the person:write scope.
5. When a prediction date's horizon has passed, online validation checks the scores against what really happened and records realized AUC.
6. Training keeps going on a schedule. On each scoring day the model starts a research run (explore) until a run does not promote a better model, or until 5 research runs have run. After that it starts one research run every research_interval_days (maintain, default 7) after the latest training run, so a run started by hand resets the clock. With research_interval_days null the model trains only when someone asks. A champion that cannot score still gets a short rescue run. training_phase and next_research_at on posthog:autoresearch-retrieve say where the model is.

In the PostHog UI the entity is a model. The API and tools call it a pipeline. Use "model" when you talk to people.