mlsanityData to 14 Oct 2026

Frugal, Flexible, Faithful: Causal Data Simulation via Frengression

arXiv 2508.01018 ↗First posted 1 Aug 2025stat.ME stat.MLcited by 2

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Abstract (arXiv)

Machine learning has revitalized causal inference by combining flexible models and principled estimators, yet robust benchmarking and evaluation remain challenging with real-world data. In this work, we introduce frengression, a deep generative realization of the frugal parameterization that models the joint distribution of covariates, treatments and outcomes around the causal margin of interest. Frengression provides accurate estimation and flexible, faithful simulation of multivariate, time-varying data; it also enables direct sampling from user-specified interventional distributions. Model consistency and extrapolation guarantees are established, with validation on real-world clinical trial data demonstrating frengression's practical utility. We envision this framework sparking new research into generative approaches for causal margin modelling.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 24 of its 54 references that are in the map. First posted on arXiv 1 Aug 2025.

Builds on

24 of 54 references.

References · 24

  1. 2015Estimation and Inference of Heterogeneous Treatment Effects using Random Forestscited by 600
  2. 2017Causal Effect Inference with Deep Latent-Variable Modelscited by 410
  3. 2016Kernel Mean Embedding of Distributions: A Review and Beyondcited by 364
  4. 2019Adapting Neural Networks for the Estimation of Treatment Effectscited by 287
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  6. 2021Discussion on "Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects" by Hahn, Murray and Carvalhocited by 77
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  12. 2021Copula Flows for Synthetic Data Generationcited by 35
12 more
  1. 2019Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulationscited by 32
  2. 2022Estimating average causal effects from patient trajectoriescited by 29
  3. 2021The Impact of the U.S. Census Disclosure Avoidance System on Redistricting and Voting Rights Analysiscited by 19
  4. 2024Longitudinal Targeted Minimum Loss-based Estimation with Temporal-Difference Heterogeneous Transformercited by 10
  5. 2021Parameterizing and Simulating from Causal Modelscited by 9
  6. 2024Marginal Causal Flows for Validation and Inferencecited by 8
  7. 2022On the role of benchmarking data sets and simulations in method comparison studiescited by 7
  8. 2025An AI-powered Bayesian generative modeling approach for causal inference in observational studiescited by 6
  9. 2015Efficient Estimation of Quantiles in Missing Data Modelscited by 5
  10. 2020Simulating longitudinal data from marginal structural models using the additive hazard modelcited by 2
  11. 2025Distributional Instrumental Variable Methodcited by 2
  12. 2023Making Differential Privacy Work for Census Data Userscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Theoretical Analysis of Engression and Reverse Markov Engressioncited by 4
  2. 2026Controllable Generative Sandbox for Causal Inferencecited by 0