# Individualized Policy Evaluation and Learning under Clustered Network Interference

arXiv: https://arxiv.org/abs/2311.02467 · HTML: https://mlsanity.com/paper/2311.02467/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 62 references that are in the map.
- First posted on arXiv 4 Nov 2023.

## Builds on (most cited first, 23 of 23)

- [Optimal and Adaptive Off-policy Evaluation in Contextual Bandits](https://mlsanity.com/paper/1612.01205/) (2016)
- [Off-policy evaluation for slate recommendation](https://mlsanity.com/paper/1605.04812/) (2016)
- [Offline Multi-Action Policy Learning: Generalization and Optimization](https://mlsanity.com/paper/1810.04778/) (2018)
- [Balanced Policy Evaluation and Learning](https://mlsanity.com/paper/1705.07384/) (2017)
- [Policy learning "without" overlap: Pessimism and generalized empirical Bernstein's inequality](https://mlsanity.com/paper/2212.09900/) (2022)
- [Semi-Parametric Efficient Policy Learning with Continuous Actions](https://mlsanity.com/paper/1905.10116/) (2019)
- [Estimating Total Treatment Effect in Randomized Experiments with Unknown Network Structure](https://mlsanity.com/paper/2205.12803/) (2022)
- [Experimental Evaluation of Individualized Treatment Rules](https://mlsanity.com/paper/1905.05389/) (2019)
- [Policy Targeting under Network Interference](https://mlsanity.com/paper/1906.10258/) (2019)
- [Heterogeneous Treatment and Spillover Effects under Clustered Network Interference](https://mlsanity.com/paper/2008.00707/) (2020)
- [Average Direct and Indirect Causal Effects under Interference](https://mlsanity.com/paper/2104.03802/) (2021)
- [Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment](https://mlsanity.com/paper/2109.11679/) (2021)
- [Design and Analysis of Bipartite Experiments under a Linear Exposure-Response Model](https://mlsanity.com/paper/2103.06392/) (2021)
- [A Graph-Theoretic Approach to Randomization Tests of Causal Effects Under General Interference](https://mlsanity.com/paper/1910.10862/) (2019)
- [Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design](https://mlsanity.com/paper/2208.05553/) (2022)
- [Doubly Robust Estimation in Observational Studies with Partial Interference](https://mlsanity.com/paper/1806.07422/) (2018)
- [Optimal Individualized Treatment Rule for Combination Treatments Under Budget Constraints](https://mlsanity.com/paper/2303.11507/) (2023)
- [Spillover Effects in the Presence of Unobserved Networks](https://mlsanity.com/paper/1708.08171/) (2017)
- [Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War](https://mlsanity.com/paper/2307.08840/) (2023)
- [Peer Encouragement Designs in Causal Inference with Partial Interference and Identification of Local Average Network Effects](https://mlsanity.com/paper/1609.04464/) (2016)
- [Causal Inference from Observational Studies with Clustered Interference](https://mlsanity.com/paper/1711.04834/) (2017)
- [Safe Policy Learning under Regression Discontinuity Designs with Multiple Cutoffs](https://mlsanity.com/paper/2208.13323/) (2022)
- [Efficient Semiparametric Estimation of Network Treatment Effects Under Partial Interference](https://mlsanity.com/paper/2004.08950/) (2020)

## Built on by (most cited first, 6 of 6)

- [Online Experimental Design With Estimation-Regret Trade-off Under Network Interference](https://mlsanity.com/paper/2412.03727/) (2024)
- [Design-Based Bandits Under Network Interference: Trade-Off Between Regret and Statistical Inference](https://mlsanity.com/paper/2510.07646/) (2025)
- [IntOPE: Off-Policy Evaluation in the Presence of Interference](https://mlsanity.com/paper/2408.13484/) (2024)
- [Towards Optimal Environmental Policies: Policy Learning under Arbitrary Bipartite Network Interference](https://mlsanity.com/paper/2410.08362/) (2024)
- [Qini Curve Estimation under Clustered Network Interference](https://mlsanity.com/paper/2502.20097/) (2025)
- [Direct Profit Estimation Using Uplift Modeling under Clustered Network Interference](https://mlsanity.com/paper/2509.01558/) (2025)

## Abstract (arXiv)

Although there is now a large literature on policy evaluation and learning, much of the prior work assumes that the treatment assignment of one unit does not affect the outcome of another unit. Unfortunately, ignoring interference can lead to biased policy evaluation and ineffective learned policies. For example, treating influential individuals who have many friends can generate positive spillover effects, thereby improving the overall performance of an individualized treatment rule (ITR). We consider the problem of evaluating and learning an optimal ITR under clustered network interference (also known as partial interference), where clusters of units are sampled from a population and units may influence one another within each cluster. Unlike previous methods that impose strong restrictions on spillover effects, such as anonymous interference, the proposed methodology only assumes a semiparametric structural model, where each unit's outcome is an additive function of individual treatments within the cluster. Under this model, we propose an estimator that can be used to evaluate the empirical performance of an ITR. We show that this estimator is substantially more efficient than the standard inverse probability weighting estimator, which does not impose any assumption about spillover effects. We derive the finite-sample regret bound for a learned ITR, showing that the use of our efficient evaluation estimator leads to the improved performance of learned policies. We consider both experimental and observational studies, and for the latter, we develop a doubly robust estimator that is semiparametrically efficient and yields an optimal regret bound. Finally, we conduct simulation and empirical studies to illustrate the advantages of the proposed methodology.