Digital Interventions use smart devices (smartphones/wearables/virtual assistants) to deliver treatments to help individuals in their everyday life. Examples of these treatments include suggestions about how to be more active in your current setting, reminders to take medications, guided mindfulness exercises and motivational messages.

However all of these treatments can interrupt your life and be a big hassle. This talk is about collecting and using data to answer questions such as "When and in which setting should an activity suggestion be sent so as to have the greatest positive impact on people? In what settings should activity suggestions not be sent?" These are all causal inference questions.

Here we discuss "micro-randomized" trials that provide data to address these causal questions. We discuss how the randomization probabilities are determined by online algorithms. In one online algorithm, the randomization probabilities are determined by a "reinforcement learning" algorithm in which the randomization probability is higher in settings in which the individual is predicted to be more responsive and lower in settings in which the individual is less responsive.

No prerequisites are required.

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