Distributed Lag Neural Additive Models
We introduce Distributed Lag Neural Additive Models (DLNAMs), neural-additive analogues of Distributed Lag Non-linear Models (DLNMs) for learning nonlinear effects distributed over lags. DLNAMs replace a prespecified spline cross-basis with neural components that learn exposure--lag response surfaces, avoiding choices of basis family, dimension, and knot placement while preserving additive interpretability and familiar distributed-lag summaries. Exp-centered input layers, smooth activations, and learned subnetwork mixtures produce smooth, locally adaptive representations; pointwise uncertainty combines a conditional last-layer Laplace approximation with between-member ensemble variation. ...
| Authors | Calle Helmersson, Shivang Pandey, Leonardo Olivetti et al. |
|---|---|
| Primary category | stat.ML |
| Published | 7 September 2026 |
| Last updated at source | 7 September 2026 |
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