Causal Data Science Lab @UIUC

I am Yonghan Jung, an assistant professor in the School of Information Sciences at UIUC and the leader of the Causal Data Science Lab. I am also a faculty affiliate at the National Center for Supercomputing Applications (NCSA) and Illinois Informatics.

Our lab studies Pragmatic Causal Inference: methods that make causal questions as usable as prediction workflows while keeping assumptions, uncertainty, and validity explicit. A data scientist often wants to know what would happen if an action changed. Answering that question requires more than prediction; it requires a causal workflow that can reason about assumptions, identify what is learnable, estimate it reliably, and support decisions.

Our current research is organized around four connected directions:

1. Partial Identification: valid causal answers under weak assumptions, including bounds and sensitivity analyses when point identification is not justified.

2. Practical Causal Learning: estimators and workflows for causal questions under mild, decision-relevant assumptions such as front-door, proxy, mediator, or overlap structure.

3. Amortized Causal Inference: simulation, pretraining, and scalable algorithms that reduce repeated graph, identification, and estimation burdens.

4. Real-World Deployment: causal systems for biomedical digital twins, health decisions, and other settings where causal answers must support action.