# Nonparametric Estimation of Population Average Dose-Response Curves using Entropy Balancing Weights for Continuous Exposures

arXiv: https://arxiv.org/abs/2003.02938 · HTML: https://mlsanity.com/paper/2003.02938/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [End-to-End Balancing for Causal Continuous Treatment-Effect Estimation](https://mlsanity.com/paper/2107.13068/) (2021)
- [Partial Identification of Dose Responses with Hidden Confounders](https://mlsanity.com/paper/2204.11206/) (2022)
- [Contrastive Balancing Representation Learning for Heterogeneous Dose-Response Curves Estimation](https://mlsanity.com/paper/2403.14232/) (2024)
- [Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights](https://mlsanity.com/paper/2406.19195/) (2024)
- [Generalized Balancing Weights via Deep Neural Networks](https://mlsanity.com/paper/2211.07533/) (2022)
- [Learning sources of variability from high-dimensional observational studies](https://mlsanity.com/paper/2307.13868/) (2023)
- [Temporal-Spatial Entropy Balancing for Causal Continuous Treatment-Effect Estimation](https://mlsanity.com/paper/2312.08670/) (2023)
- [Causal Generalization of Continuous Treatment Effects under Covariate Shift](https://mlsanity.com/paper/2608.19383/) (2026)

## Abstract (arXiv)

Weighted estimators are commonly used for estimating exposure effects in observational settings to establish causal relations. These estimators have a long history of development when the exposure of interest is binary and where the weights are typically functions of an estimated propensity score. Recent developments in optimization-based estimators for constructing weights in binary exposure settings, such as those based on entropy balancing, have shown more promise in estimating treatment effects than those methods that focus on the direct estimation of the propensity score using likelihood-based methods. This paper explores recent developments of entropy balancing methods to continuous exposure settings and the estimation of population dose-response curves using nonparametric estimation combined with entropy balancing weights, focusing on factors that would be important to applied researchers in medical or health services research. The methods developed here are applied to data from a study assessing the effect of non-randomized components of an evidence-based substance use treatment program on emotional and substance use clinical outcomes.