Learning Counterfactual Representations for Estimating Individual Dose-Response Curves

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.

published at AAAI 2020

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Cited by in corpus (7)

• Causal machine learning for predicting treatment outcomes
• DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation
• CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Events
• De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network
• A Deep Subgrouping Framework for Precision Drug Repurposing via Emulating Clinical Trials on Real-world Patient Data
• STOAT: Spatial-Temporal Probabilistic Causal Inference Network
• Sources of Gain: Decomposing Performance in Conditional Average Dose Response Estimation