BGM-IV: A Bayesian generative model for nonlinear causal inference
A new latent Bayesian method outperforms alternatives in high-dimensional causal analysis.
Instrumental variable (IV) regression is essential for causal estimation under endogeneity, but modern problems involve nonlinear effects and many covariates. Existing methods often struggle with high-dimensional settings. BGM-IV, proposed by Guyue Luo and Qiao Liu, takes a different approach: it models the entire generative process in a causally structured latent space. It infers distinct latent components that capture shared confounding, outcome-specific variation, treatment-specific variation, and covariate-only nuisance. To handle endogeneity, it replaces the standard confounded outcome likelihood with a pseudo-likelihood that averages over instrument-induced treatment values.
On benchmarks, BGM-IV matches existing methods in low-dimensional scenarios and achieves the best performance in high-dimensional covariate regimes. The results demonstrate that structured latent generative modeling offers a principled and effective strategy for nonlinear IV estimation with rich covariates. The code is open-sourced, enabling adoption in fields like econometrics, epidemiology, and any domain requiring robust causal inference from complex observational data.
- Reframes nonlinear IV regression as posterior inference in a causally structured latent space.
- Separates latent components into confounding, outcome, treatment, and covariate nuisance to better model endogeneity.
- Achieves state-of-the-art performance in high-dimensional covariate regimes across multiple benchmarks.
Why It Matters
Enables more accurate causal estimates in high-dimensional, nonlinear settings, advancing econometrics and AI-driven decision-making.