With Daniel Manela and Linying Yang!.

Please join us at our UAI virtual poster on:. Testing generalizability in causal inference. July 22nd, Poster session 1, 16:00-18:30 BRT, 19:00-21:30 UTZ.

Using simulations and statistical testing, our framework is robust and avoids over-reliance on conventional metrics, providing statistical safeguards for decision making.

- A testing framework that statistically evaluates the transportability of both mean and distributional regression methods, and complements MSE.

- A statistical framework to evaluate the generalizability of  high-dimensional causal inference algorithms by targeting low-dimensional causal margins.

In this paper, we propose a statistical  method to test model generalizability. Our contributions include.- A method to simulate (semi-)synthetic data across two domains,  with explicitly conditional outcome density and the causal margin of target domain.

**Poster Alert**. Generalizability is a common problem in causality, as we often want to generalize causal findings from one domain to another. However, current metrics like MSE can only do model comparison rather than answer directly whether a model is generalizable.

Validating causal models often relies on simplified assumptions. Frugal Flows address this by using normalising flows to generate datasets that exactly meet user-defined causal constraints, ensuring rigorous benchmarking.

Frugal Flows generate synthetic datasets with:.•Realistic data patterns.•Customisable ATEs and treatment heterogeneity.•Unobserved confounding effects.These features make them ideal for benchmarking causal models.

Frugal Flows allow for inference, and offer a new approach to causality by:.•Leveraging normalising flows for flexible data generation.•Exactly parameterising ATEs and confounding effects.•Bridging machine learning and causal inference.

Excited to share our latest research: Marginal Causal Flows for Validation and Inference. We introduce Frugal Flows—a normalising flows based generative model that directly parameterises causal effects, enabling realistic causal dataset simulation.

What’s purple and commutes?. An abelian grape.

RT @MoMath1: What's your favorite math joke?.

Everyone knows it’s gold!.

Postdoc positions in Oxford to work with me, @cwcyau, Chris Holmes, George Nicholson and others. Very exciting project!. (Advertised through psychiatry, but jobs not tied to that area!).

RT @WrayNaomi: New Oxford-Aarhus-Copenhagen collaboration - not yet launched - 2 postdocs, 2 senior postdocs and postdoc coordinator in….

Half term fun @MuseumofOxford !

RT @ProfEspie: Keeping your eyes open can help you fall asleep faster. Here’s why Delighted to provide the science….

RT @TheresaRSmith: In related news. I'll be hiring soon! Please get in touch if you are a looking for a post doc position broadly in the a….

RT @UncertaintyInAI: We invite proposals for tutorials at #UAI2024 by March 15th. Topics include, not limited to.- Causal Inference/Discov….