# Double Machine Learning for Causal Inference under Shared-State Interference

arXiv: https://arxiv.org/abs/2504.08836 · HTML: https://mlsanity.com/paper/2504.08836/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 49 references that are in the map.
- First posted on arXiv 10 Apr 2025.

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

- [Prediction-Powered Inference](https://mlsanity.com/paper/2301.09633/) (2023)
- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Nonparametric methods for doubly robust estimation of continuous treatment effects](https://mlsanity.com/paper/1507.00747/) (2015)
- [Design and analysis of experiments in networks: Reducing bias from interference](https://mlsanity.com/paper/1404.7530/) (2014)
- [Experimental Design in Two-Sided Platforms: An Analysis of Bias](https://mlsanity.com/paper/2002.05670/) (2020)
- [Design and Analysis of Switchback Experiments](https://mlsanity.com/paper/2009.00148/) (2020)
- [Causal inference for social network data](https://mlsanity.com/paper/1705.08527/) (2017)
- [Applied Causal Inference Powered by ML and AI](https://mlsanity.com/paper/2403.02467/) (2024)
- [Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and Beyond](https://mlsanity.com/paper/1912.12945/) (2019)
- [Markovian Interference in Experiments](https://mlsanity.com/paper/2206.02371/) (2022)
- [Experimenting in Equilibrium](https://mlsanity.com/paper/1903.02124/) (2019)
- [Theoretical analysis of deep neural networks for temporally dependent observations](https://mlsanity.com/paper/2210.11530/) (2022)
- [Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach](https://mlsanity.com/paper/2006.05591/) (2020)
- [Correcting for Interference in Experiments: A Case Study at Douyin](https://mlsanity.com/paper/2305.02542/) (2023)
- [Reducing Symbiosis Bias Through Better A/B Tests of Recommendation Algorithms](https://mlsanity.com/paper/2309.07107/) (2023)
- [Experimenting under Stochastic Congestion](https://mlsanity.com/paper/2302.12093/) (2023)
- [Randomization and The Pernicious Effects of Limited Budgets on Auction Experiments](https://mlsanity.com/paper/1605.09171/) (2016)
- [Reducing Marketplace Interference Bias Via Shadow Prices](https://mlsanity.com/paper/2205.02274/) (2022)
- [Central limit theorems via Stein's method for randomized experiments under interference](https://mlsanity.com/paper/1804.03105/) (2018)
- [Estimating Treatment Effects under Algorithmic Interference: A Structured Neural Networks Approach](https://mlsanity.com/paper/2406.14380/) (2024)
- [Spillover Effects in Experimental Data](https://mlsanity.com/paper/2001.05444/) (2020)
- [Semiparametric inference for impulse response functions using double/debiased machine learning](https://mlsanity.com/paper/2411.10009/) (2024)
- [Treatment Effect Estimation with Observational Network Data using Machine Learning](https://mlsanity.com/paper/2206.14591/) (2022)

## Built on by (most cited first, 4 of 4)

- [Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?](https://mlsanity.com/paper/2507.11891/) (2025)
- [Extending Prediction-Powered Inference through Conformal Prediction](https://mlsanity.com/paper/2510.16166/) (2025)
- [Estimating Treatment Effects with Independent Component Analysis](https://mlsanity.com/paper/2507.16467/) (2025)
- [Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality](https://mlsanity.com/paper/2605.01907/) (2026)

## Abstract (arXiv)

Researchers and practitioners often wish to measure treatment effects in settings where units interact via markets and recommendation systems. In these settings, units are affected by certain shared states, like prices, algorithmic recommendations or social signals. We formalize this structure, calling it shared-state interference, and argue that our formulation captures many relevant applied settings. Our key modeling assumption is that individuals' potential outcomes are independent conditional on the shared state. We then prove an extension of a double machine learning (DML) theorem providing conditions for achieving efficient inference under shared-state interference. We also instantiate our general theorem in several models of interest where it is possible to efficiently estimate the average direct effect (ADE) or global average treatment effect (GATE).