Exponential Family Synthetic Controls
We develop exponential family synthetic controls (EFSC), a distributional version of synthetic controls for a panel of datasets. Each cell of the panel corresponds to a dataset drawn from an exponential family whose natural parameters factorize probabilistically across units and times. We estimate the latent factors using black-box variational inference. This replaces the usual weighted-average view of synthetic controls with a flexible probabilistic model that operates on full distributions. We propose causal estimands based on divergences between pre- and post-intervention distributions induced by the posterior of the natural parameters, together with distributional placebo tests to sup...
| Authors | Hector Rodriguez-Deniz, David M. Blei |
|---|---|
| Primary category | stat.ML |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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