# Efficient Semiparametric Estimation of Network Treatment Effects Under Partial Interference

arXiv: https://arxiv.org/abs/2004.08950 · HTML: https://mlsanity.com/paper/2004.08950/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [Individualized Policy Evaluation and Learning under Clustered Network Interference](https://mlsanity.com/paper/2311.02467/) (2023)
- [Nonparametric efficient inference for network quantile causal effects under partial interference](https://mlsanity.com/paper/2604.13008/) (2026)

## Abstract (arXiv)

Recently, many estimators for network treatment effects have been proposed. But, their optimality properties in terms of semiparametric efficiency have yet to be resolved. We present a simple, yet flexible asymptotic framework to derive the efficient influence function and the semiparametric efficiency lower bound for a family of network causal effects under partial interference. An important corollary of our results is that one of the existing estimators by Liu et al. (2019) is locally efficient. We also present other estimators that are efficient and discuss results on adaptive estimation. We conclude by using the efficient estimators to study the direct and spillover effects of conditional cash transfer programs in Colombia.