# Minimax designs for causal effects in temporal experiments with treatment habituation

## TL;DR

This paper introduces minimax optimal randomized designs for estimating causal effects in temporal experiments where treatment habituation occurs, using a non-parametric causal inference framework.

## Contribution

It proposes novel randomized designs specifically tailored for temporal experiments with habituation, achieving minimax optimality without parametric assumptions.

## Key findings

- Designs are proven to be minimax optimal within a broad class of practical designs.
- The approach does not rely on parametric modeling of outcomes.
- Applicable to experiments with repeated treatments and habituation effects.

## Abstract

Randomized experiments are the gold standard for estimating the causal effects of an intervention. In the simplest setting, each experimental unit is randomly assigned to receive treatment or control, and then the outcomes in each treatment arm are compared. In many settings, however, randomized experiments need to be executed over several time periods such that treatment assignment happens at each time period. In such temporal experiments, it has been observed that the effects of an intervention on a given unit may be large when the unit is first exposed to it, but then it often attenuates, or even vanishes, after repeated exposures. This phenomenon is typically due to units' habituation to the intervention, or some other general form of learning, such as when users gradually start to ignore repeated mails sent by a promotional campaign. This paper proposes randomized designs for estimating causal effects in temporal experiments when habituation is present. We show that our designs are minimax optimal in a large class of practical designs. Our analysis is based on the randomization framework of causal inference, and imposes no parametric modeling assumptions on the outcomes.