$β$-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap
This addresses a key challenge in causal inference for fields like biostatistics, but appears incremental as it builds on existing VAE frameworks for prognostic scores.
The paper tackles the problem of identifying and estimating treatment effects under limited overlap, where subjects with certain features belong to a single treatment group, by proposing β-Intact-VAE, a new type of variational autoencoder that models a prognostic score and proves identification of individualized treatment effects, with comparisons to recent methods on (semi-)synthetic datasets.
As an important problem in causal inference, we discuss the identification and estimation of treatment effects (TEs) under limited overlap; that is, when subjects with certain features belong to a single treatment group. We use a latent variable to model a prognostic score which is widely used in biostatistics and sufficient for TEs; i.e., we build a generative prognostic model. We prove that the latent variable recovers a prognostic score, and the model identifies individualized treatment effects. The model is then learned as β-Intact-VAE--a new type of variational autoencoder (VAE). We derive the TE error bounds that enable representations balanced for treatment groups conditioned on individualized features. The proposed method is compared with recent methods using (semi-)synthetic datasets.