180123 VO Causal Evidence and Explanation Across the Sciences (2024W)

Exam structure: As the course consists of both introductory material in philosophy of science (Lectures 1-3) and more advanced material on causal evidence and explanation (Lectures 4-11), the written exam will consist of one randomly chosen question from the first block and two questions from the second block. Question 1 is worth 30% of the grade, the other two questions are worth 35% each. Each question will contain a descriptive part (describing a specific philosophical problem or defining a concept) and an argumentative part (defending a particular position on the problem).

Students are allowed to use notes based on lectures/lecture slides during the exam. No other aids will be permitted.

Preparation materials: lecture notes; lecture slides on Moodle; Stanford Encyclopedia of Philosophy; background readings listed in the syllabus. Doing the background reading is advisable to enhance your understanding of lecture material but having a detailed knowledge of the readings is not required.

Block 1 – Introduction to philosophy of science
- What are the goals of the sciences? Briefly describe each goal and give some examples.
- What is inductive reasoning in science? Explain how it differs from deductive reasoning.
- What is the problem of induction according to Hume? Can it be solved?
- What kinds of statements are meaningful according to the logical empiricists? What is Hume’s fork?
- Explain Popper’s falsificationist approach to hypothesis testing and relate it to the induction/deduction distinction.
- What is the deductive-nomological (DN) model of explanation?
- In what way do the problems of explanatory irrelevance and explanatory asymmetry support a causal account of explanation over the deductive-nomological account?

Block 2 – Causation and causal evidence
- What is Woodward’s interventionist account of causal explanation?
- What are stability and specificity of causal relationships? Reflect on how they relate to the traditional goals of science, such as explanation and prediction.
- What is the difference between observational and experimental data and how does this distinction relate to the problem of causal evidence?
- What is idealisation in scientific models (incl. causal models) and why is it often necessary?
- Describe the basic methodology of randomised controlled trials (RCTs). Which areas of science rely on RCTs as a method for causal inference?
- What is internal and external validity of RCT findings?
- Are there good reasons to have a strict evidence hierarchy with RCTs as the “gold standard” for causal inference?
- What are quasi-experimental research designs (e.g. in econometrics)? Briefly describe one design (e.g. instrumental variables estimation; difference-in-differences; regression discontinuity) and explain how it avoids (or does not avoid) the common pitfalls of causal inference such as the confounding problem.
- What is the role of mechanistic causal evidence, particularly in policymaking?
- What are some of the challenges of causal inference in the social sciences like psychology?
- What is causal selection and how do our values inform which causal factors we pick out for explanation?