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180123 VO Causal Evidence and Explanation Across the Sciences (2024W)
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Details
max. 25 Teilnehmer*innen
Sprache: Englisch
Prüfungstermine
- N Freitag 31.01.2025 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
- Freitag 07.03.2025 11:30 - 13:00 Hörsaal 2i NIG 2.Stock C0228
- Freitag 06.06.2025 11:30 - 13:00
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- Freitag 11.10. 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
- Freitag 25.10. 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
- Freitag 08.11. 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
- Freitag 15.11. 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
- Freitag 22.11. 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
- Freitag 29.11. 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
- Freitag 06.12. 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
- Freitag 13.12. 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
- Freitag 10.01. 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
- Freitag 17.01. 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
- Freitag 24.01. 11:30 - 13:00 Hörsaal 3F NIG 3.Stock
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Scientific disciplines routinely seek to generate evidence of causal relationships: from masking reducing the spread of Covid-19 to social connections improving psychological well-being. Among its other uses, such evidence helps us design good policies, i.e. intervene effectively into the world around us. However, how should causal evidence be generated and what methods are suitable for the task? What exactly does it mean to establish a causal relationship? And what makes for a good causal explanation in different scientific contexts? This lecture course begins by introducing some of the fundamental themes in philosophy of science, such as the goals and methods of scientific inquiry. These themes will then be used as a springboard for reflecting on the nature of causal evidence and explanation across scientific disciplines, including biomedical and health sciences; social sciences such as psychology, economics, and political science; and social policy research. The course will focus especially on the tensions and disagreements surrounding the generation of causal evidence in these different areas. Should methods such as randomized controlled trials (RCTs) have a privileged place in the evidence hierarchy? What are the limitations of RCTs when it comes to informing policy? Is mechanistic evidence required for developing successful interventions? Are quasi-experimental research designs (e.g. natural experiments) indispensable for causal inference in the social sciences where experimentation is often limited by practical and ethical constraints? Finally, the course also considers the dissatisfactions prompted by what some consider to be an excessive scientific focus on licensing causal claims. Does the rigorous investigation of causal relationships come at the expense of other epistemic goods, such as identifying robust phenomena or understanding qualitative aspects of human experience? Are there important research questions (e.g. related to societal problems such as discrimination) that cannot be satisfactorily addressed by the methods of establishing causality? All of these themes are explored in the course by engaging with the relevant philosophical literature as well as case studies from recent science.
Art der Leistungskontrolle und erlaubte Hilfsmittel
The final grade will be based on a written exam. Questions will be based on the lectures, the PDFs of the slides made available on the e-learning platform, and the background reading.
Mindestanforderungen und Beurteilungsmaßstab
The examination for the lecture will be graded on a basis of 100 points in total.
100-89 points Excellent
88-76 points Good
75-63 points Satisfactory
62-50 points Sufficient
49-0 points Unsatisfactory (fail)
100-89 points Excellent
88-76 points Good
75-63 points Satisfactory
62-50 points Sufficient
49-0 points Unsatisfactory (fail)
Prüfungsstoff
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?
- 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?
Literatur
Angrist, J., & Pischke, J. (2010). The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. Journal of Economic Perspectives, 24(2), 3-30.
Bokulich, A. (2018). Searching for non-causal explanations in a sea of causes. In Reutlinger, A., & Saatsi, J. (Eds.) Explanation Beyond Causation: Philosophical Perspectives on Non-Causal Explanations, 141-163.
Clarke, B., Gillies, D., Illari, P., Russo, F., & Williamson, J. (2014). Mechanisms and the evidence hierarchy. Topoi, 33, 339-360.
Deaton, A., and N. Cartwright. Understanding and misunderstanding randomized controlled trials. Social Science & Medicine 210 (2018): 2-21.
Eronen, M. I. (2020). Causal discovery and the problem of psychological interventions. New Ideas in Psychology, 59, 100785.
Gannett, L. (1999). What's in a cause?: The pragmatic dimensions of genetic explanations. Biology and Philosophy, 14, 349-373.
Godfrey-Smith, P. (2003). Theory and reality : an introduction to the philosophy of science. Chicago: Univ. of Chicago Press.
Grüne-Yanoff, T. (2016). Why behavioural policy needs mechanistic evidence. Economics & Philosophy, 32(3), 463-483.
Lewens, T. (2016). The meaning of science: An introduction to the philosophy of science. Hachette UK.
Okasha, S. (2002). Philosophy of science : a very short introduction. Oxford: Oxford Univ. Press.
Skow, B. (2016). “Scientific Explanation”. In Paul Humphreys (ed.), The Oxford Handbook of Philosophy of Science.
Potochnik, A. (2017). Idealization and the aims of science. Chicago: University of Chicago Press.
Ramachandran, M. T. (2020). The logic of randomised controlled trials in the social sciences. Social Scientist, 48(1/2 (560-561), 41-52.
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological science, 1(1), 27-42.
Solomon, M., Simon, J. R., & Kincaid, H. (2016). The Routledge companion to philosophy of medicine. Boca Raton, FL : Routledge.
Woodward, J. (2003). Making things happen : a theory of causal explanation. Oxford ; New York : Oxford University Press.
Bokulich, A. (2018). Searching for non-causal explanations in a sea of causes. In Reutlinger, A., & Saatsi, J. (Eds.) Explanation Beyond Causation: Philosophical Perspectives on Non-Causal Explanations, 141-163.
Clarke, B., Gillies, D., Illari, P., Russo, F., & Williamson, J. (2014). Mechanisms and the evidence hierarchy. Topoi, 33, 339-360.
Deaton, A., and N. Cartwright. Understanding and misunderstanding randomized controlled trials. Social Science & Medicine 210 (2018): 2-21.
Eronen, M. I. (2020). Causal discovery and the problem of psychological interventions. New Ideas in Psychology, 59, 100785.
Gannett, L. (1999). What's in a cause?: The pragmatic dimensions of genetic explanations. Biology and Philosophy, 14, 349-373.
Godfrey-Smith, P. (2003). Theory and reality : an introduction to the philosophy of science. Chicago: Univ. of Chicago Press.
Grüne-Yanoff, T. (2016). Why behavioural policy needs mechanistic evidence. Economics & Philosophy, 32(3), 463-483.
Lewens, T. (2016). The meaning of science: An introduction to the philosophy of science. Hachette UK.
Okasha, S. (2002). Philosophy of science : a very short introduction. Oxford: Oxford Univ. Press.
Skow, B. (2016). “Scientific Explanation”. In Paul Humphreys (ed.), The Oxford Handbook of Philosophy of Science.
Potochnik, A. (2017). Idealization and the aims of science. Chicago: University of Chicago Press.
Ramachandran, M. T. (2020). The logic of randomised controlled trials in the social sciences. Social Scientist, 48(1/2 (560-561), 41-52.
Rohrer, J. M. (2018). Thinking clearly about correlations and causation: Graphical causal models for observational data. Advances in Methods and Practices in Psychological science, 1(1), 27-42.
Solomon, M., Simon, J. R., & Kincaid, H. (2016). The Routledge companion to philosophy of medicine. Boca Raton, FL : Routledge.
Woodward, J. (2003). Making things happen : a theory of causal explanation. Oxford ; New York : Oxford University Press.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Di 28.01.2025 14:46