Conditional Attribute Transformers jointly estimate next-token probabilities and conditional attribute values for autoregressive sequence models, enabling credit assignment, counterfactuals, and steerable generation in one pass.
An encoding generative modeling approach to dimension reduction and covariate adjustment in causal inference with observational studies
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BGM-IV performs nonlinear IV regression by inferring causally structured latent components and replacing the outcome likelihood with an instrument-averaged pseudo-likelihood, showing strongest results in high-dimensional covariate regimes.
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Conditional Attribute Estimation with Autoregressive Sequence Models
Conditional Attribute Transformers jointly estimate next-token probabilities and conditional attribute values for autoregressive sequence models, enabling credit assignment, counterfactuals, and steerable generation in one pass.
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BGM-IV: an AI-powered Bayesian generative modeling approach for instrumental variable analysis
BGM-IV performs nonlinear IV regression by inferring causally structured latent components and replacing the outcome likelihood with an instrument-averaged pseudo-likelihood, showing strongest results in high-dimensional covariate regimes.