Causal Inference

CausalDS is a benchmark generator for agentic causal data science: every problem is generated fresh in its entirety, with tasks spanning all three rungs of Pearl’s hierarchy that involve significant tool use. Exam composition is a free parameter: it can be tailored to a specific goal or grounded in real-world corpora. The benchmark jointly tests symbolic causal reasoning, data-science execution, uncertainty quantification, epistemic abstention, and coding/tool use.

We have a new preprint out on benchmarking agentic causal data-science.

Approaching an unknown communication system has been accepted to Royal Society Open Science.

A talk at the Simons Institute on using deep generative models to study sperm whale communication.

We propose a novel methodology - Causal Disentanglement with Extreme Values (CDEV) - to identify representations learned by GANs. When trained on raw whale communication, it finds - for the first time - specific acoustic attributes that might serve as carriers of meaning.

We have a pre-print out based on our work with Project CETI, an initiative to decipher sperm whale communication using machine learning. In the paper, we combine approaches that help discover how known properties of human language are learned by generative models when trained on labeled speech audio data with methodology inspired by causal inference and apply it to the communication system of sperm whales, for which we do not have any such ground truth.