arXiv 11 Oct 2021 · Statistics — Machine Learning · 2 citations (OpenAlex)
arXiv:2110.05225 · PDF · DOI · OpenAlex · Extracted main text
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 \beta-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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Ben B Hansen (2008) The prognostic analogue of the propensity score | 0.928 | 5 | 3 | 80% |
| 2 | Uri Shalit, Fredrik D Johansson, and David Sontag (2017) Estimating individual treatment effect: generalization bounds and algorithms | 0.874 | 9 | 6 | 67% |
| 3 | Fredrik D Johansson, Uri Shalit, Nathan Kallus, and David Sontag (2020) Generalization bounds and representation learning for estimation of potential outcomes and causal effects | 0.874 | 6 | 3 | 67% |
| 4 | Alexander D'Amour and Alexander Franks (2021) Deconfounding scores: Feature representations for causal effect estimation with weak overlap | 0.843 | 3 | 3 | 100% |
| 5 | Ming-Yueh Huang and Kwun Chuen Gary Chan (2017) Joint sufficient dimension reduction and estimation of conditional and average treatment effects | 0.843 | 3 | 3 | 100% |
| 6 | Christos Louizos, Uri Shalit, Joris M Mooij, David Sontag, Richard Z… (2017) Causal effect inference with deep latent-variable models | 0.769 | 11 | 4 | 45% |
| 7 | Alexander D’Amour, Peng Ding, Avi Feller, Lihua Lei, and Jasjeet Sek… (2020) Overlap in observational studies with high-dimensional covariates | 0.737 | 4 | 3 | 50% |
| 8 | Jinsung Yoon, James Jordon, and Mihaela van der Schaar (2018) GANITE: Estimation of individualized treatment effects using generative adversarial nets | 0.737 | 3 | 3 | 67% |
| 9 | Jennifer L Hill (2011) Bayesian nonparametric modeling for causal inference | 0.644 | 3 | 2 | 67% |
| 10 | Max H Farrell (2015) Robust inference on average treatment effects with possibly more covariates than observations | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 79 scored citations.