Controllable Generative Sandbox for Causal Inference
Method validation and study design in causal inference rely on synthetic data with known counterfactuals. Existing simulators trade off distributional realism, the ability to capture mixed-type and multimodal tabular data, against causal controllability, including explicit control over overlap, unmeasured confounding, and treatment effect heterogeneity. We introduce CausalMix, a variational generative framework that closes this gap by coupling a mixture of Gaussian latent priors with data-type-specific decoders for continuous, binary, and categorical variables. The model incorporates explicit causal controls: an overlap regularizer shaping propensity-score distributions, alongside direct ...
| Authors | Qi Zhang, Harsh Parikh, Ashley Naimi et al. |
|---|---|
| Primary category | stat.ME |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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