Causality and causal inference

1. Causality and causal inference 4th session, reading group in qualitative methods 11/7-2008

2. What is causality • Ontology • What is causality? • The interpretations of Hume: constant conjunction or skepticism • Regularity views and counterfactual accounts • One definition (David Lewis); A causes B if • A ¤->B • ~A ¤->~B • Applies to singular events ontologically, but can be generalized. Start with the idiosyncratic, and generalizations as descriptions.

3. How can we infer causality • Epistemology • Causation and covariation • Different causal structures that might lead to covariation • The role of time • Epi-phenomenality • What can we infer from spatial variation? Unit homogeneity and constant effect assumptions. • In general: Ceteris Paribus clauses (Gerring, ch6) • What if there is no variation temporally or spatially? Counterfactual claims • The intermingling of empirical evidence, theories and models, design structure and sometimes “common sense” • Causal inferences are always made with a degree of uncertainty. Insight into the social world, and non-observability (Hume again) • Some general problems: Measurement error, omitted variable bias (KKV) or other potential mechanisms at work (Elster), endogeneity problems, multi-colinearity

4. Inference continued • Experiments and quasi-experiments • The long and tedious (but enlightening) text from Campbell and Stanley dwells on the potential pit-falls with different experimental set-ups. Some key-words: learning effects, selection effects, history and time trends, unobserved variables • Statistics • Econometrics, pit-falls and remedies • Unit homogeneity? Fixed effects • Endogeneity? Lagging variables and 2SLS • Linearity? Functional form, matching • Conjunctural causality? Interaction terms..(Ragin as alternative) • Comparative method • Case-studies • The role of additional evidence, knowledge of phenomena, triangulation of different sorts and pieces of evidence (arriving at empirical implications)

5. Causal effects • KKV and counterfactuals..systematic and unsystematic components. Average causal effect and variation in causal effect. Contingencies of causal effects. • Equifinality and conjunctural causality: does not require different metaphysics, but puts different demands on design for inference • Causal effect is prior to mechanisms. The latter gives us more fine grained knowledge about processes, but we need to start with something to explain. A B..through which mechanisms? A x y B z

6. Causal mechanisms • Mayntz: Mahoney found 24 different def by 21 diff authors! • Should be used on linked activities. Linking events (Elster) • Sequences as important, but looping and feedback: Paul David and QWERTY • Causal mechanisms and the plausibility of our inferences. • Methodological individualism and the role of actors • Mayntz: specify “level of reality”, “degree of conceptual abstraction” and “assumed scope of application”. • Specification and classification: Cumulative knowledge • One example from Mayntz: macro-micro, micro-micro and micro-macro. • Mechanisms based explanation and causal effect-based explanation are complementary rather than contradictory • Some tools: social psychology, cognitive psychology, game theory • What if multiple equilibria and many potential cognitive mechanism at work? Empirical evidence. Be aware of and report plausible alternatives and uncertainty of inf.

7. Elster and mechanisms • The dynamic aspect of the social sciences and the quest for ever more fine-grained explanations. • The variety of mechanisms..pre-emptive mechanisms..Find the one that is operating • Explanation and prediction • Explanation and narratives..evidence • Explanation and functionalism

8. Gerring and process tracing • Triangulating bits and pieces of different types of evidence • Often hard to quantify, because of the different nature of the evidence. Still try to qualitatively assess uncertainty.. • Process tracing and detective work. • Theories and predictions of different phenomena..various levels of analysis and aggregation, various spheres of social life • Often leans on general assumptions about the social world: Stringency, inference and the need to explicate these assumptions: folk psychology and Keynes in economic history. An interesting one: people believe what they report and report truthfully! • Advice from Gerring: • 1) Clarify the argument (visual diagrams?), • 2) verify each stage of the model, evaluate uncertainty. Focus on dubious parts.