The document discusses the causal interpretation of statistical models in social research. It outlines different perspectives from staunch causalists to moderate skeptics. Interpreting a statistical model causally is described as an epistemic activity to decide if a model is valid, rather than determining a physical causal relation. The causal interpretation depends on the statistical information and machinery used to make inferences from the model. Keeping statistical and causal inferences distinct is important, while acknowledging the role of background knowledge in interpretation.

1. On the causal interpretation of statistical models in social research Federica Russo Philosophy, Kent joint work with Alessio Moneta Max Plank Institute, Jena

2. Overview Background The unbearable lightness of causality Associational vs Causal Models Statistical vs Causal information Interpreting a statistical model causally The epistemic stance 2

3. The dawn of historyof causal modelling Staunch causalists Quetelet, Durkheim, Wright …, Blalock, Duncan, … Moderate skeptics Pearl, Heckman, Hoover, … … and the evergreen question: When and how can we draw causal conclusions from statistics? 3

4. Intuitions and insights 4

5. 5

6. Information Statistical A summary of data Inferential statistics (sample to population) Adequate and parsimonious description of the phenomenon Statistical dependence Causal Opening the ‘black box’ From association to causation Statistical information to provide the formalised empirical evidence Background ‘constraints’ Tests 6

7. All nice but … A vicious circle introduced? Not quite … How much background knowledge? Just the right amount … 7

8. What’s interpretinga statistical model causally?

9. The philosophers’ huntfor truthmakers … that is, what makes a causal claim true Difference-makers Probabilistic, counterfactual, manipulation Mechanisms 9

10. Anything wrong with the hunt? Conceptual analysis in philosophy of causality What explicates the concept of ‘causality’ What makes causal claims true What is causality, metaphysically Conceptual analysts failed to distinguish between evidence and concept lost on the way epistemic practices 10

11. What’s interpretinga statistical model causally? An epistemic activity …

12. In the footprints of epistemic theorists Evidence and concept Evidential pluralism: difference-making and mechanistic considerations Conceptual monism: causation is an inferential map Causality: an epistemic category to interpret the world rather than a physical relation in our ontology 12

13. Interpreting in causal terms … … is deciding whether a model is valid or not Making successful inferences Not merely dependent on the physical existence of mechanisms Mechanisms have explanatory import Mechanistic and difference-making evidential components are tangled 13

14. The causal interpretation is model-dependent Causal conclusions depend on the statistical information and machinery from which they are inferred Not a bad thing after all Causation is not a ‘all or nothing’ affair Nor a ‘once and for all’ affair 14

15. To sum up The causal interpretation of statistical model: An evergreen question from the staunch causalists to the moderate skeptics Methodological arguments: Associational vs Causal Models Statistical vs Causal Information Philosophical arguments Against the hunt for truthmakers For an ‘epistemic’ stance 15

16. To conclude We gain a lot (arguably, hopefully) We don’t hamper in an endless hunt for mechanisms and for difference-makers Statistical and causal inferences are distinct and must be kept separated We acknowledge the large role played by the ‘elaboration of the mind’ (Durkheim) 16