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

arXiv: https://arxiv.org/abs/1908.03531 · HTML: https://mlsanity.com/paper/1908.03531/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [NURD: Negative-Unlabeled Learning for Online Datacenter Straggler Prediction](https://mlsanity.com/paper/2203.08339/) (2022)

## Abstract (arXiv)

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.