#  Qi Zhang (Applied Stats Workshop) 

 



####  calendar\_today Date and Time 

 **November 11, 2026** 

 12:00PM - 01:30PM EST 

####  pin\_drop Location 

 **CGIS Knafel Building, Room K354**  



 

 [ Join via Zoom arrow\_circle\_right ](https://harvard.zoom.us/j/97290769975?pwd=jQcyPMGalc45ZuTaZ7A87bBlvQZB30.1) 

 



 

### Speaker & Title

Qi Zhang (Harvard), "Controllable Generative Sandbox for Causal Inference"

### Abstract

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 parameterizations of confounding strength and effect heterogeneity. This unified objective preserves fidelity to the observed data while enabling factorial manipulation of causal mechanisms, allowing overlap, confounding strength, and treatment effect heterogeneity to be varied independently at design time. Across evaluation settings, CausalMix achieves strong distributional fidelity on mixed-type tables while providing stable, fine-grained causal control. We demonstrate practical utility in a comparative safety study of metastatic castration-resistant prostate cancer treatments, using CausalMix to benchmark estimators under calibrated data-generating processes, tune hyperparameters, and conduct simulation-based power analyses under targeted treatment effect heterogeneity scenarios.





 

The Applied Statistics Workshop (Gov 3009) meets all academic year on Wednesdays. This workshop is a forum for advanced graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers.  
  
All interested Harvard affiliates are invited to attend. Lunch will be provided.



 

 



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