Deconfounding Scores and Representation Learning for Causal Effect Estimation with Weak Overlap
Overlap, also known as positivity, is a key condition for causal treatment effect estimation. Many popular estimators suffer from high variance and become brittle when features differ strongly across treatment groups. This is especially challenging in high dimensions: the curse of dimensionality can make overlap implausible. To address this, we propose a class of feature representations called deconfounding scores, which preserve both identification and the target of estimation; the classical propensity and prognostic scores are two special cases. We characterize the problem of finding a representation with better overlap as minimizing an overlap divergence under a deconfounding score con...
| Authors | Oscar Clivio, Alexander D'Amour, Alexander Franks et al. |
|---|---|
| Primary category | stat.ML |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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