1. A non-manipulationist account of inviariance Federica Russo Philosophy, Kent

2. Overview Causal assessment and manipulationism Invariance under intervention and the manipulationist dilemma A non-manipulationist account of invariance Invariance under changes of the environment Invariance vs variation Invariance vs regularity

3. The problem of causal assessment E.Coli causes food poisoning Gravitational interactions between Hearth and Moon cause tides Ageing populations have an effect on pension and health care systems Causal assessment is the question of whether A causes B. Granted, it typically needs support from how questions. (RWT)

4. Manipulationism Information about the results of interventions is of utmost importance for explanation or causal assessment

5. Manipulationism and causal assessment Empirical generalisations Change-relating relations between variables Spurious? Accidental? Invariance Empirical generalisations must show some invariability in order to be causal Intervention Empirical generalisations must be invariant under specified interventions on the cause 5

6. A manipulationist dilemma Horn 1. Conceptual manipulationism Truth conditions Truth conditions: X causes Y if, and only if, manipulations on X accordingly yield changes on Y Unilluminating as to the methods Horn 2. Methodological manipulationism method A method for causal assessment: Were manipulations on X yield changes on Y, then then we’d be entitled to infer that X causes Y a)Strictly interpreted Stuck back into Horn 1 b)Charitably interpreted Stuck back into Horn (a) Disingenuous rationale of causal assessment 6

7. A NON-MANIPULATIONIST ACCOUNT OF INVARIANCE

8. Invariance under changes Observational contexts environment Changes in the environment Background knowledge and preliminary analyses of data suggest how to partition the population to test for invariance in different ‘environments’ Stability of the model parameters across chosen partitions of the population

9. Example: self-rated health in Baltic countries ‘Self-rated health’, the response variable (effect), directly depends on ‘Education’, ‘Alcohol consumption’, ‘Locus of control’, ‘Psychological distress’, and ‘Physical health’

10. What ‘environments’? Four age groups [18-29, 30-44, 45-59, 60+]Both gendersLocal individual and foreigners (mainly Russians)…

11. What results? Causal factors alcohol consumption, physical health, psychological health, psychological distress, education, locus of control, and social support remarkable stable impact had a remarkable stable impact on self-rated health across different environments the different Baltic countries, across the time-frames analysed, across gender, ethnicity, or age group.

12. Take home message Nota bene This subsumes rather, than rule out, ‘experimental’ changes, i.e. interventions Changes in the putative effects due to targeted interventions in the putative cause Manipulations are not the building block of causal assessment. They are a good tool, when they can be performed Manipulations are not the building block of causal assessment. They are a good tool, when they can be performed

13. Invariance of what? change-relating Invariance of the change-relating generalisations Variational epistemology / methodology for invariant change-relating generalisations

14. Variational epistemology Truth conditions Truth conditions – conceptual analysis Conditions under which a causal claim is true ‘X causes Y’ iff were we to manipulate … Rationale Rationale – epistemology/methodology Notion underlying causal reasoning/methods Are there joint variations between X and Y? Are those variations spurious / invariant / regular / due to intervention on X …? 14

15. Variational methodology Y =  X+  Variational reading Variations in Y are accompanied by variations in X May be just observational. Impose further constraints Manipulationist reading (derived) Manipulations on X make X vary such that Y varies accordingly Joint variations between X-Y are due to manipulations Counterfactual reading (derived) Were we to vary X, Y would accordingly vary Joint variations between X-Y are hypothetical 15

16. So far … Causal assessment and the troubles of manipulationism Non-manipulationist invariance to rescue Variational epistemology/methodology to underpin invariance Next time … Invariance vs regularity (or, debunking a false myth)