PHIL 740 · also STA 740
Causality and Causal Inference: Foundations, Identification, and Complex Systems
Alexander Volfovsky, Benjamin Eva, Reuben Edwin Stern
Description from DukeHub
An advanced doctoral introduction to the mathematical, statistical, computational, and philosophical foundations of causality and causal inference. Topics range from potential outcomes and structural causal models to partial identification, causal decision theory, and causal explanation. Emphasis is placed not only on the scientific application of these tools and frameworks, but also on the meaning, status, and plausibility of the assumptions on which they rest. The course examines proofs of identification, relationships among alternative causal frameworks, and recent efforts to extend causal modeling to new domains, including complex systems in which effects are context-dependent, mechanisms are partially observed, or interventions alter the systems under study.
Skills
- data and statisticsdescription: "statistical" and 2 more
AI-resilient: 1 of 5
- No: Physical, lab or field work
- No: Live work with people
- Yes: Judgment someone is accountable for · "decision"
- No: A craft you practice with your hands or body
- No: Small, in-person format
The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works
More in PHIL
- Introduction to Philosophy PHIL 103S
- How to Understand Arguments PHIL 105CC-01
- Why Obey the Law PHIL 108CNS
- AI Impact on Human Experience PHIL 113CNS-01
- Medical Ethics PHIL 118CNS
- Logic PHIL 150