# Integrating Nearest Neighbors with Neural Network Models for Treatment Effect Estimation

arXiv: https://arxiv.org/abs/2305.06789 · HTML: https://mlsanity.com/paper/2305.06789/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 11 May 2023.

## Builds on (most cited first, 9 of 9)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [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)
- [Benchmarking Framework for Performance-Evaluation of Causal Inference Analysis](https://mlsanity.com/paper/1802.05046/) (2018)
- [An evaluation framework for comparing causal inference models](https://mlsanity.com/paper/2209.00115/) (2022)
- [An improved neural network model for treatment effect estimation](https://mlsanity.com/paper/2205.11106/) (2022)
- [Kuramoto model based analysis reveals oxytocin effects on brain network dynamics](https://mlsanity.com/paper/2105.08288/) (2021)

## Built on by (most cited first, 1 of 1)

- [C-XGBoost: A tree boosting model for causal effect estimation](https://mlsanity.com/paper/2404.00751/) (2024)

## Abstract (arXiv)

Treatment effect estimation is of high-importance for both researchers and practitioners across many scientific and industrial domains. The abundance of observational data makes them increasingly used by researchers for the estimation of causal effects. However, these data suffer from biases, from several weaknesses, leading to inaccurate causal effect estimations, if not handled properly. Therefore, several machine learning techniques have been proposed, most of them focusing on leveraging the predictive power of neural network models to attain more precise estimation of causal effects. In this work, we propose a new methodology, named Nearest Neighboring Information for Causal Inference (NNCI), for integrating valuable nearest neighboring information on neural network-based models for estimating treatment effects. The proposed NNCI methodology is applied to some of the most well established neural network-based models for treatment effect estimation with the use of observational data. Numerical experiments and analysis provide empirical and statistical evidence that the integration of NNCI with state-of-the-art neural network models leads to considerably improved treatment effect estimations on a variety of well-known challenging benchmarks.