# Multiple Randomization Designs: Estimation and Inference with Interference

arXiv: https://arxiv.org/abs/2112.13495 · HTML: https://mlsanity.com/paper/2112.13495/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2021.

## Built on by (most cited first, 20 of 20)

- [Markovian Interference in Experiments](https://mlsanity.com/paper/2206.02371/) (2022)
- [Correcting for Interference in Experiments: A Case Study at Douyin](https://mlsanity.com/paper/2305.02542/) (2023)
- [Multi-Armed Bandits with Network Interference](https://mlsanity.com/paper/2405.18621/) (2024)
- [Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments](https://mlsanity.com/paper/2403.17285/) (2024)
- [A Common Misassumption in Online Experiments with Machine Learning Models](https://mlsanity.com/paper/2304.10900/) (2023)
- [Graph-Dependent Regret Bounds in Multi-Armed Bandits with Interference](https://mlsanity.com/paper/2503.07555/) (2025)
- [Balancing Interference and Correlation in Spatial Experimental Designs: A Causal Graph Cut Approach](https://mlsanity.com/paper/2505.20130/) (2025)
- [Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?](https://mlsanity.com/paper/2507.11891/) (2025)
- [A Two-Part Machine Learning Approach to Characterizing Network Interference in A/B Testing](https://mlsanity.com/paper/2308.09790/) (2023)
- [Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach](https://mlsanity.com/paper/2310.17496/) (2023)
- [Network Synthetic Interventions: A Causal Framework for Panel Data Under Network Interference](https://mlsanity.com/paper/2210.11355/) (2022)
- [A Two-armed Bandit Framework for A/B Testing](https://mlsanity.com/paper/2507.18118/) (2025)
- [Differences-in-Neighbors for Network Interference in Experiments](https://mlsanity.com/paper/2503.02271/) (2025)
- [The Price of Isolation: Estimating the Ecosystem Cost of Symmetric Two-Sided A/B Testing](https://mlsanity.com/paper/2608.04432/) (2026)
- [Causal Estimation of User Learning in Personalized Systems](https://mlsanity.com/paper/2306.00485/) (2023)
- [A Simple Model to Estimate Sharing Effects in Social Networks](https://mlsanity.com/paper/2409.12203/) (2024)
- [Qini Curve Estimation under Clustered Network Interference](https://mlsanity.com/paper/2502.20097/) (2025)
- [Unbiased Platform-Level Causal Estimation for Search Systems: A Competitive Isolation PSM-DID Framework](https://mlsanity.com/paper/2511.01329/) (2025)
- [Robust Sequential Experimental Design for A/B Testing](https://mlsanity.com/paper/2605.12899/) (2026)
- [Content Exploration Beyond the Feed: Creator Supply and the Shared Corpus](https://mlsanity.com/paper/2608.29430/) (2026)

## Abstract (arXiv)

Completely randomized experiments, originally developed by Fisher and Neyman in the 1930s, are still widely used in practice, even in online experimentation. However, such designs are of limited value for answering standard questions in marketplaces, where multiple populations of agents interact strategically, leading to complex patterns of spillover effects. In this paper, we derive the finite-sample properties of tractable estimators for "Simple Multiple Randomization Designs" (SMRDs), a new class of experimental designs which account for complex spillover effects in randomized experiments. Our derivations are obtained under a natural and general form of cross-unit interference, which we call "local interference". We discuss the estimation of main effects, direct effects, and spillovers, and present associated central limit theorems.