Run adaptive experiments, learn on the fly, send more traffic to the best options — and even personalize choices using context.
🎰 Multi-Armed & Contextual Bandits in Evidential
We’re excited to announce that Evidential now supports Multi-Armed Bandit and Contextual Bandit experiments — alongside our frequentist A/B tests.
What are Bandits?
Think of each version of your intervention as "arm" in a slot machine. Your "reward" when you pull the arm is the outcome you observe when you deploy your intervention to your beneficiaries.
A multi-armed bandit algorithm learns which arm pays off best and automatically directs more participants toward it -- so you optimize your interventions on the go, rather than at the end of your experiment.
A contextual bandit goes one step further: it takes into account information about participants — such as demographics or behavior — to decide which arm is likely to work best for that specific user.
Why this matters
- Traditional A/B tests split participants evenly across variations, wasting time and resources on poor options.
- Multi-armed bandits quickly identify the best variation and shift traffic toward it.
- Contextual bandits personalize in real time, matching people with the intervention most likely to help them.
Together, these approaches reduce the audience size and time needed to discover what works, maximizing impact while still maintaining experimental rigor.
How to use it
It’s just as straightforward as creating any other experiment in Evidential: when you set up an experiment, simply choose Multi-Armed Bandit or Contextual Bandit in the experiment type dropdown. Evidential takes care of the complex math under the hood — no extra coding required.
What problems can this solve?
- Optimizing messaging, timing, or program variations in real time.
- Testing multiple strategies when outcomes are scarce or expensive to measure.
- Personalizing interventions based on participant characteristics or circumstances.
✨ With bandits, you don’t just test — you learn and improve continuously while running your experiment.
Released: August 20, 2025