ECCS: Exposing Critical Causal Structures

For data systems that support causal queries, high quality causal models are essential to more reliable query results. The golden standard for establishing causal models for scientific domain data is carefully designed experiments, often relying on interventions in a laboratory setting. However, interventional experiments can often be not plausible while building a causal model for custom domain data. Therefore, people rely on extracting models from observational data. Standard statistical causal discovery algorithms often do not scale to accomodate the number of variables and the volume of data in custom scenarios. Most causal discovery algorithms also cater to downstream tasks with more indirect measures of accuracy. In this project, we are interested in developing framework for interactively refine a causal model for such custom domain data systems. The framework aims to efficiently use itsinteractivity budget to minimize biases in given Average Treatment Effect (ATE) queries that the user is interested in.

Read the Paper (GUIDE-AI 2024 at SIGMOD 2024) | View BibTeX

Project Participants

Markos Markakis, Sylvia Zhang, Rana Shahout, Trinity Gao, Chunwei Liu, Ibrahim Sabek, Michael Cafarella