causal discovery 1concept embeddings 1high-dimensional inference 1interpretability 1multiple hypothesis testing 1unstructured data 1
From the 1 of 3 linked papers with an AI index.
3 papers
econ.EM2026
Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
Jacob Carlson
The paper proposes a framework that converts unstructured data into sparse, interpretable concept embeddings and then applies high‑dimensional multiple hypothesis testing with sele…
econ.EM2026
When are time series predictions causal? The potential system and dynamic causal effects
Jacob Carlson, Neil Shephard
The potential system is a nonparametric time series model for assessing the causal impact of moving an assignment at time on an outcome at future time , accounting for the…
econ.EM2026
A Unifying Framework for Robust and Efficient Inference with Unstructured Data
Jacob Carlson, Melissa Dell
To analyze unstructured data (text, images, audio, video), economists typically first extract low-dimensional structured features with a neural network. Neural networks do not make…