Explanation: Provides an answer to a why question: What makes for an epistemically “good” answer to a why question?
Ideographic versus nomothetic explanations: Ideographic explanations seek to “account for” an individual event, often relying on “thick description” and contextually, sensitive analysis. Nomothetic explanations subsume (and thereby explain) a larger number of cases under a more general law. Methodologically, this distinction is reflected in (historical) case studies versus large-N quantitative studies, whereby the former focuses on the qualitative aspects of a case, while the latter seeks to code and quantify relevant aspects of the cases under investigation. If a nomothetic relationship is due to an accidental generalization, in what sense do nomothetic relationships really explain an effect?
Explaining versus understanding: Causes explain events, and reasons help understand actions (which may translate into events). Explaining involves subsuming a cause and an effect under a law-like relationship, thereby allowing the cause to “explain” the effect via the (actual or postulated!) law-like relationship. Understanding makes use of the hermeneutical method and seeks to make sense of decisions and actions from an agent’s point of view. Does this therefore mean that unique events can never explained?
Causes versus reasons: Causes can be just about anything, while reasons are closely tied to human agency, volition, rationality, purpose, incentives, preferences, affections, goals, interests. How can one sensibly generate inter-subjective agreement as to what the reasons for an action were, and can these reasons not equally be subsumed under a law-like generalization, thereby effectively turning them into causes?
Regularity view of causation and determinism: Regularity may not be causal, and causal regularity may not be deterministic. In what they though does a statistical generalization explain an event if it makes the event less likely than not (e.g. p =0.2)?
Explanations and hypotheses. Data, information, evidence confirm, disconfirm, corroborate or falsify hypotheses. But how exactly do they do this, and how can one be sure that they really do so, and to what degree do they do so?
(Relatedly) Justification, evidence, confirmation: Do these terms really all mean the same?
Regularity view of causation and determinism: Regularity may not be causal, and causal regularity may not be deterministic. In what they though does a statistical generalization explain an event if it makes the event less likely than not (e.g. p =0.2)?
Explanations and hypotheses. Data, information, evidence confirm, disconfirm, corroborate or falsify hypotheses. But how exactly do they do this, and how can one be sure that they really do so, and to what degree do they do so?
(Relatedly) Justification, evidence, confirmation: Do these terms really all mean the same?