Kaisa_2012_3_photo by Veikko Somerpuro

Enrol
13.2.2020 at 09:00 - 9.3.2020 at 23:59

Timetable

Here is the course’s teaching schedule. Check the description for possible other schedules.

DateTimeLocation
Mon 9.3.2020
10:15 - 11:45
Wed 11.3.2020
12:15 - 13:45
Mon 16.3.2020
10:15 - 11:45
Wed 18.3.2020
12:15 - 13:45
Mon 23.3.2020
10:15 - 11:45
Wed 25.3.2020
12:15 - 13:45
Mon 30.3.2020
10:15 - 11:45
Wed 1.4.2020
12:15 - 13:45
Mon 6.4.2020
10:15 - 11:45
Wed 8.4.2020
12:15 - 13:45
Mon 20.4.2020
10:15 - 11:45
Wed 22.4.2020
12:15 - 13:45
Mon 27.4.2020
10:15 - 11:45
Wed 29.4.2020
12:15 - 13:45

Description

Introduction to Philosophy of Science, Methods of Philosophical Thinking, Introduction to Logic, Philosophy of Social Sciences

Students will become familiar with the main theories of causation.

They will be able to critically assess the virtues and deficiencies of these theories in relation to concrete scientific goals.

They will become familiar with methods of causal inference currently in use in the social sciences.

Teaching can be organised in any period, resources permitting.

The course examines the main philosophical accounts of causality: regularity, probabilistic, counterfactual, process/mechanistic, and interventionist accounts. The essential features of these theories are analysed with a focus on their potential to explain, predict, or control social phenomena. The sessions are in seminar format. Two seminars per week. During the first five weeks, each week there will be one seminar devoted to discuss the main features of a causal theory, and a second seminar to discuss potential and actual applications of the theory in the context of the social sciences. The last two weeks will be devoted to explore some standard methods of causal inference currently employed in the social sciences, in particular probabilistic forms of causal modelling, potential outcomes, and econometric techniques.

Week 1: Regularities and causal laws

Monday (9 March): Regularity theories

  • Hume and Mill on the notion of causation
  • What is a causal law?

Wednesday (11 March): INUS conditions

  • John Mackie’s regularity account
  • Are there causal regularities in the social sciences?

Week 2: Probabilistic causality

Monday (16 March): Conditional probability increasing

  • Suppes’s probabilistic theory of causation
  • Discussion of some counterexamples to probabilistic accounts

Wednesday (18 March): Causal laws and effective strategies

  • Cartwright’s probabilistic theory
  • Can probabilistic theories help us understand social phenomena?

Week 3: Counterfactual theories

Monday (23 March): David Lewis on causation

  • Causation and the semantics of counterfactuals

Wednesday (25 March): Counterfactual causation in the social sciences

  • Singular versus general causation
  • Counterfactuals in the study of history and law

Week 4: Process and Mechanistic theories

Monday (30 March): Casual processes

  • The Salmon-Dowe’s account of causal processes
  • Can this approach be used outside the natural sciences?

Wednesday (1 April): Mechanisms in the social sciences

  • What is a mechanism?
  • Are there mechanisms in the social realm?

Week 5: Interventionist theory

Monday (6 April): Reductive and non-reductive theories

  • Old and new interventionists theories
  • Woodward’s interventionist account

Wednesday (8 April): Interventions in the social sciences

  • Causal interventionism in macroeconomic analysis

Week 6: Causal inference in the social sciences

Monday (20 April): Process tracing

  • The search for social mechanisms and external validity
  • Process tracing versus experimental designs

Wednesday (22 April): Potential-outcomes framework

  • Counterfactuals and the fundamental problem of causal inference
  • Experimental interpretation of observational data

Week 7: Causal inference and policy-making

Monday (27 April): Causal efficacy versus policy effectiveness

  • Causal inference and policy evaluation
  • The roles of evidence in science and policy-making

Wednesday (29 April): TBA

Presence at all the sessions and writing a one-page overview per session of the mandatory readings will account for 60% of the final grade. The overviews should be uploaded to the Moodle’s course page or sent via e-mail (luis.mireles-flores@helsinki.fi) by 18.00 hrs. of the day before each session. There will be no exam at the end of the course. An essay to be submitted after the course on a topic to be agreed with the instructor will account for the remaining 40% of the grade.

Main Readings (will be selected for each session from the following sources):

Beebee, Helen, Christopher R. Hitchcock, and Peter Menzies. The Oxford Handbook of Causation. Oxford: Oxford University Press.

Cartwright, Nancy D. 1979. Causal laws and effective strategies. Noûs, 13 (4): 419-437.

Cartwright, Nancy D., and Sophia Efstathiou. 2011. Hunting causes and using them: is there no bridge from here to there? International Studies in the Philosophy of Science, 25 (3): 223-241.

Gasking, Douglas. 1955. Causation and recipes. Mind, 64 (256): 479-487.

Hitchcock, Christopher. R. 2001. A tale of two effects. The Philosophical Review, 110 (3): 361-396.

Hoover, Kevin D. 2001. Causality in macroeconomics. Cambridge: Cambridge University Press.

Lewis, David. 1973. Causation. Journal of Philosophy, 70 (17): 556-567.

Mackie, John. 1974. The cement of the universe: a study of causation. Oxford (UK): Clarendon.

Mäki, Uskali. 1992. The market as an isolated causal process: a metaphysical ground for realism. In Austrian economics: tensions and new directions, eds. Bruce J. Caldwell, and Stephan Boehm. Dordrecht: Kluwer Academic Publishers, 35-59.

Mill, J. S. 1874 [1843]. A system of logic. New York: Harper.

Morgan, Stephen L., and Christopher Winship. 2007. Counterfactuals and causal inference: methods and principles for social research. Cambridge (UK): Cambridge University Press.

Pearl, Judea. 2000. Causality: models, reasoning, and inference. Cambridge: Cambridge University Press.

Reiss, Julian. 2005. Causal instrumental variables and interventions. Philosophy of Science, 72 (5): 964-976.

Reiss, Julian. 2009. Counterfactuals, thought experiments and singular causal analysis in history. Philosophy of Science, 76 (5): 712-723.

Reiss, Julian. 2015. Causation, evidence, and inference. London: Routledge.

Salmon, Wesley. C. 1980. Causality: production and propagation. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, 2: 49-69.

Scheines, Richard. 1997. An introduction to causal inference. In Causality in crisis?, eds. V. McKim, and S. Turner. Notre Dame (IN): University of Notre Dame Press, 185-200.

Sosa, Ernest, and Michael Tooley. 1993. Causation. Oxford: Oxford University Press.

Steel, Daniel. 2004. Social mechanisms and causal inference. Philosophy of the Social Sciences, 34 (1): 55-78.

Suppes, Patrick. 1970. A probabilistic theory of causality. Amsterdam: North-Holland.

Woodward, James. 2003. Making things happen: a theory of causal explanation. Oxford: Oxford University Press.

Given that there will be no exam, students will be mainly graded according to the quality of their written and oral comments and the final essay.

The course teacher chooses the language of instruction; the course is usually in English.

The course can be completed by attending the lectures, writing an essay or taking an Examinarium examination.

Luis Mireles-Flores