Estimating Individual Dose-Response Curves under Unobserved Confounders from Observational Data
This addresses a crucial limitation in causal inference for domains like healthcare and social sciences by enabling continuous treatment effect estimation with unobserved confounders, representing a novel method for a known bottleneck.
The paper tackles the problem of estimating individual dose-response curves for continuous treatments in the presence of unobserved confounders from observational data, and results show that ContiVAE outperforms existing methods by up to 62% on semi-synthetic datasets.
Estimating an individual's potential response to continuously varied treatments is crucial for addressing causal questions across diverse domains, from healthcare to social sciences. However, existing methods are limited either to estimating causal effects of binary treatments, or scenarios where all confounding variables are measurable. In this work, we present ContiVAE, a novel framework for estimating causal effects of continuous treatments, measured by individual dose-response curves, considering the presence of unobserved confounders using observational data. Leveraging a variational auto-encoder with a Tilted Gaussian prior distribution, ContiVAE models the hidden confounders as latent variables, and is able to predict the potential outcome of any treatment level for each individual while effectively capture the heterogeneity among individuals. Experiments on semi-synthetic datasets show that ContiVAE outperforms existing methods by up to 62%, demonstrating its robustness and flexibility. Application on a real-world dataset illustrates its practical utility.