We want to evaluate the use of Causality algorithms as described in “Causal Inference in Statistics” by Judea Pearl et al.
- Survey latest techniques and research for Causal Modeling and Causal Inferencing
- Develop tools for Causal Modeling including:
o Statistical Independence and Conditional Independence Tests
o Causal Modeling representation
o Model Identifiability Detection
o Interventional Calculus solver
o Counterfactual Calculus solver
o Causal Modeling User Interface
- Identify multiple datasets for real-world analysis
- Conduct analysis of selected datasets and compare with known ground truth where available
- Publish paper(s) on the research
- Test and document code for general release
The successful applicants will demonstrate:
- Proficiency with probability and statistics
- Understanding of the basic concepts and techniques of Causality and Causal Inference
- Experience with Machine Learning algorithms