Causal Data Science Lab @UIUC

I am Yonghan Jung, an assistant professor in the School of Information Science at the UIUC and the leader of the Causal Data Science Lab. My research focuses on developing causal data science methods to understand causal effects in complex, imperfect data, with broad applications in trustworthy AI and healthcare science. Our primary research areas include:

1. Identification and Estimation under Real-World Imperfections — advancing frameworks that reliably infer causal effects despite challenges such as unmeasured confounding, limited overlap, or complex data-generating processes.
2. Robust and Scalable Estimation — designing variance-stable, computationally efficient methods that scale to high-dimensional and large-scale datasets.
3. Trustworthy Inference — developing methods to ensure transparent, reliable, fair, and interpretable inference in high-stakes scientific and societal settings.
4. Causal Decision-Making — creating algorithms that leverage causal reasoning to support efficient, robust, and generalizable decision making in complex, high-stakes environments.
5. Causal AI for Diverse Modalities — integrating causal reasoning with modern AI to enhance trustworthy causal inference methods, enabling richer analyses of images, text, temporal data, and other complex modalities.