Disentangled Double Machine Learning for Accurate Causal Effect Estimation
Confounding bias is a key challenge in causal effect estimation from observational data. Double Machine Learning (DML) addresses this issue by estimating treatment and outcome nuisance functions, constructing treatment and outcome residuals, and estimating causal effects from the residuals. However, DML often produces biased and unstable estimates in highdimensional or finite-sample scenarios. One reason is that DML estimates nuisance functions using all covariates without disentangling distinct latent factors, resulting in unreliable nuisance function estimation. Another is that imprecise nuisance estimation further introduces residual dependence between the treatment residual and the re...
| Authors | Guodu Xiang, Kui Yu, Yujie Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 24 May 2026 |
| Last updated at source | 24 May 2026 |
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