Causality X

(Same as Statistics M169.) Strongly recommended: Mathematics 170E or Statistics 100A. Causal claims—claims that one thing affects another—are central to life choices, science, and policy. They are very difficult to substantiate and often misleading. The data revolution makes various forms of association easier to discover, but in many cases does not help to affirm whether an observed association reflects the implied causal relationship. The larger datasets grow, the more easily non-causal associations can rise to the level of being noticed and mistakenly used as causal. Study builds conceptual and formal analytical skills required to evaluate causal claims through a focus on conceptual work and more technical analytic methods. Reading and critiquing studies and reporting of studies at a conceptual level. Use of online tools to review statistics and R-coding. Improvement of conceptual understandings by examining the statistical tools used to understand, work with, and make causal claims in a variety of circumstances.

P/NP or letter grading.