# Policy design in experiments with unknown interference

arXiv: https://arxiv.org/abs/2011.08174 · HTML: https://mlsanity.com/paper/2011.08174/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2020.

## Built on by (most cited first, 5 of 5)

- [Higher-Order Causal Message Passing for Experimentation with Complex Interference](https://mlsanity.com/paper/2411.00945/) (2024)
- [Statistical Inference under Performativity](https://mlsanity.com/paper/2505.18493/) (2025)
- [Towards Optimal Environmental Policies: Policy Learning under Arbitrary Bipartite Network Interference](https://mlsanity.com/paper/2410.08362/) (2024)
- [Debiased Regression for Root-N-Consistent Conditional Mean Estimation](https://mlsanity.com/paper/2411.11748/) (2024)
- [Adaptive Policy Learning Under Unknown Network Interference](https://mlsanity.com/paper/2605.11191/) (2026)

## Abstract (arXiv)

This paper studies experimental designs for estimation and inference on policies with spillover effects. Units are organized into a finite number of large clusters and interact in unknown ways within each cluster. First, we introduce a single-wave experiment that estimates the marginal effect of a change in treatment probabilities, taking spillover effects into account. Using the marginal effect, we propose a policy optimality test and improve the population's average outcome through marginal changes. Second, we design a multi-wave experiment to estimate optimal treatment rules, with strong regret guarantees. We provide an implementation in a large-scale field experiment studying climate adaptation.