# Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

## TL;DR

This paper introduces a neural network-based method for estimating individual dose-response curves across multiple treatments with continuous dosages, advancing personalized response predictions in various fields.

## Contribution

It presents a novel approach for learning counterfactual representations applicable to multiple treatments with continuous dosages, along with new metrics, model criteria, and benchmarks.

## Key findings

- Sets new state-of-the-art in dose-response estimation
- Develops performance metrics and model selection criteria
- Provides open benchmarks for future research

## Abstract

Estimating what would be an individual's potential response to varying levels of exposure to a treatment is of high practical relevance for several important fields, such as healthcare, economics and public policy. However, existing methods for learning to estimate counterfactual outcomes from observational data are either focused on estimating average dose-response curves, or limited to settings with only two treatments that do not have an associated dosage parameter. Here, we present a novel machine-learning approach towards learning counterfactual representations for estimating individual dose-response curves for any number of treatments with continuous dosage parameters with neural networks. Building on the established potential outcomes framework, we introduce performance metrics, model selection criteria, model architectures, and open benchmarks for estimating individual dose-response curves. Our experiments show that the methods developed in this work set a new state-of-the-art in estimating individual dose-response.