# Causal Inference in Network Economics

arXiv: https://arxiv.org/abs/2109.11344 · HTML: https://mlsanity.com/paper/2109.11344/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 3 of 3)

- [Experimenting in Equilibrium](https://mlsanity.com/paper/1903.02124/) (2019)
- [Global Convergence to the Equilibrium of GANs using Variational Inequalities](https://mlsanity.com/paper/1808.01531/) (2018)
- [Regularized Off-Policy TD-Learning](https://mlsanity.com/paper/2006.05314/) (2020)

## Built on by (most cited first, 3 of 3)

- [Universal Decision Models](https://mlsanity.com/paper/2110.15431/) (2021)
- [Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory](https://mlsanity.com/paper/2209.06262/) (2022)
- [On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property](https://mlsanity.com/paper/2207.02917/) (2022)

## Abstract (arXiv)

Network economics is the study of a rich class of equilibrium problems that occur in the real world, from traffic management to supply chains and two-sided online marketplaces. In this paper we explore causal inference in network economics, building on the mathematical framework of variational inequalities, which is a generalization of classical optimization. Our framework can be viewed as a synthesis of the well-known variational inequality formalism with the broad principles of causal inference