# Auto-G-Computation of Causal Effects on a Network

arXiv: https://arxiv.org/abs/1709.01577 · HTML: https://mlsanity.com/paper/1709.01577/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 16 of 16)

- [Learning Causal Effects on Hypergraphs](https://mlsanity.com/paper/2207.04049/) (2022)
- [Learning Individual Treatment Effects under Heterogeneous Interference in Networks](https://mlsanity.com/paper/2210.14080/) (2022)
- [Using Embeddings for Causal Estimation of Peer Influence in Social Networks](https://mlsanity.com/paper/2205.08033/) (2022)
- [Estimating Treatment Effects Under Heterogeneous Interference](https://mlsanity.com/paper/2309.13884/) (2023)
- [Unraveling the Interplay between Carryover Effects and Reward Autocorrelations in Switchback Experiments](https://mlsanity.com/paper/2403.17285/) (2024)
- [Spatial Deconfounder: Interference-Aware Deconfounding for Spatial Causal Inference](https://mlsanity.com/paper/2510.08762/) (2025)
- [Be Aware of the Neighborhood Effect: Modeling Selection Bias under Interference](https://mlsanity.com/paper/2404.19620/) (2024)
- [Treatment Effect Estimation with Observational Network Data using Machine Learning](https://mlsanity.com/paper/2206.14591/) (2022)
- [Bayesian Estimation of Causal Effects Using Proxies of a Latent Interference Network](https://mlsanity.com/paper/2505.08395/) (2025)
- [Recursive Equations For Imputation Of Missing Not At Random Data With Sparse Pattern Support](https://mlsanity.com/paper/2507.16107/) (2025)
- [IntOPE: Off-Policy Evaluation in the Presence of Interference](https://mlsanity.com/paper/2408.13484/) (2024)
- [Network Causal Effect Estimation In Graphical Models Of Contagion And Latent Confounding](https://mlsanity.com/paper/2411.01371/) (2024)
- [Causally Fair Node Classification on Non-IID Graph Data](https://mlsanity.com/paper/2505.01652/) (2025)
- [Peer Effect Estimation in the Presence of Simultaneous Feedback and Unobserved Confounders](https://mlsanity.com/paper/2508.09154/) (2025)
- [Causal Inference for Sequential Settings under Interference and Latent Confounding](https://mlsanity.com/paper/2607.14940/) (2026)
- [A Characterization of the Orthocomplement of the Tangent Space of Semiparametric Markov Models](https://mlsanity.com/paper/2607.23439/) (2026)

## Abstract (arXiv)

Methods for inferring average causal effects have traditionally relied on two key assumptions: (i) the intervention received by one unit cannot causally influence the outcome of another; and (ii) units can be organized into non-overlapping groups such that outcomes of units in separate groups are independent. In this paper, we develop new statistical methods for causal inference based on a single realization of a network of connected units for which neither assumption (i) nor (ii) holds. The proposed approach allows both for arbitrary forms of interference, whereby the outcome of a unit may depend on interventions received by other units with whom a network path through connected units exists; and long range dependence, whereby outcomes for any two units likewise connected by a path in the network may be dependent. Under network versions of consistency and no unobserved confounding, inference is made tractable by an assumption that the network's outcome, treatment and covariate vectors are a single realization of a certain chain graph model. This assumption allows inferences about various network causal effects via the auto-g-computation algorithm, a network generalization of Robins' well-known g-computation algorithm previously described for causal inference under assumptions (i) and (ii).