# Estimation of individual causal effects in network setup for multiple treatments

arXiv: https://arxiv.org/abs/2312.11573 · HTML: https://mlsanity.com/paper/2312.11573/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Fast Computation of Wasserstein Barycenters](https://mlsanity.com/paper/1310.4375/) (2013)
- [Learning Counterfactual Representations for Estimating Individual Dose-Response Curves](https://mlsanity.com/paper/1902.00981/) (2019)
- [Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks](https://mlsanity.com/paper/1810.00656/) (2018)
- [Learning Individual Causal Effects from Networked Observational Data](https://mlsanity.com/paper/1906.03485/) (2019)

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

- [KANITE: Kolmogorov-Arnold Networks for ITE estimation](https://mlsanity.com/paper/2503.13912/) (2025)
- [I See, Therefore I Do: Estimating Causal Effects for Image Treatments](https://mlsanity.com/paper/2412.06810/) (2024)
- [Disentangled Graph Autoencoder for Treatment Effect Estimation](https://mlsanity.com/paper/2412.14497/) (2024)
- [Treatment Effect Estimation with Differentiated Networked Effect on Graph Data](https://mlsanity.com/paper/2605.24358/) (2026)

## Abstract (arXiv)

We study the problem of estimation of Individual Treatment Effects (ITE) in the context of multiple treatments and networked observational data. Leveraging the network information, we aim to utilize hidden confounders that may not be directly accessible in the observed data, thereby enhancing the practical applicability of the strong ignorability assumption. To achieve this, we first employ Graph Convolutional Networks (GCN) to learn a shared representation of the confounders. Then, our approach utilizes separate neural networks to infer potential outcomes for each treatment. We design a loss function as a weighted combination of two components: representation loss and Mean Squared Error (MSE) loss on the factual outcomes. To measure the representation loss, we extend existing metrics such as Wasserstein and Maximum Mean Discrepancy (MMD) from the binary treatment setting to the multiple treatments scenario. To validate the effectiveness of our proposed methodology, we conduct a series of experiments on the benchmark datasets such as BlogCatalog and Flickr. The experimental results consistently demonstrate the superior performance of our models when compared to baseline methods.