# An improved neural network model for treatment effect estimation

arXiv: https://arxiv.org/abs/2205.11106 · HTML: https://mlsanity.com/paper/2205.11106/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 23 May 2022.

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

- [Causal Effect Inference with Deep Latent-Variable Models](https://mlsanity.com/paper/1705.08821/) (2017)
- [Learning Representations for Counterfactual Inference](https://mlsanity.com/paper/1605.03661/) (2016)
- [Adapting Neural Networks for the Estimation of Treatment Effects](https://mlsanity.com/paper/1906.02120/) (2019)

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

- [Deep Causal Learning: Representation, Discovery and Inference](https://mlsanity.com/paper/2211.03374/) (2022)
- [Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation](https://mlsanity.com/paper/2305.06789/) (2023)
- [C-XGBoost: A tree boosting model for causal effect estimation](https://mlsanity.com/paper/2404.00751/) (2024)

## Abstract (arXiv)

Nowadays, in many scientific and industrial fields there is an increasing need for estimating treatment effects and answering causal questions. The key for addressing these problems is the wealth of observational data and the processes for leveraging this data. In this work, we propose a new model for predicting the potential outcomes and the propensity score, which is based on a neural network architecture. The proposed model exploits the covariates as well as the outcomes of neighboring instances in training data. Numerical experiments illustrate that the proposed model reports better treatment effect estimation performance compared to state-of-the-art models.