Linying Yang releases paper on ‘frengression’

Linying Yang has released a paper, Frugal, Flexible, Faithful: Causal Data Simulation via Frengression, which uses a generative method for simulating causal datasets. This builds on the frugal parameterization (Evans and Didelez, 2024) in various ways:

• it is frugal: it makes use of variation independent quantities, so these can be switched without compromising the interpretation;

• it is flexible: in our case it uses neural networks, but in principle it can make use of any method;

• it is marginal: the estimand is marginally causal, i.e. it is of the form $Y(t)$ not $Y(t) | Z=z$;

• it is faithful: the estimand can be exchanged for any other, while still retaining the structure of the original data;

• it is longitudinal: longitudinal and survival data can be estimated/simulated, as well as the static case;

• it is extrapolative: continuous treatments can be extended under an additive noise model;

• it is privacy preserving: it supports differential privacy.

We show that it is competitive with a range of other, more specific, generative methods over a suite of these settings.

The work is joint with Xinwei Shen (UW) and Robin Evans.