Levels of Inference: Identification, Explanation, and the Architecture of Causal Knowledge in Political Science
Gailmard (2026) and Dowding and Miller (2026) draw attention to a methodological gap that political science has yet to adequately address: causal identification, on its own, does not constitute causal explanation. Both contributions help clarify the gap. We extend their analyses in three ways. First, we identify three places where Gailmard’s framework relies on conceptual commitments that operate implicitly within his formal apparatus. The coherence equivalence used in his proofs is stronger than the three coherence properties he states; the bundle reading of theoretical models gives Proposition 5 its content but imposes a tolerance condition that the framework does not address; and Proposition 6 on generalization supplies a necessary-and-sufficient condition for generalization without specifying when that condition obtains. Second, we extend Dowding and Miller’s explanatory pluralism by locating constitutive explanation and explanation by constraint within a structured inferential framework, and we argue that equilibrium explanation comes in distinct varieties — equifinality and comparative statics — that play different roles in political science formal theory. Third, we introduce a nested modeling framework that separates three levels — data, conceptual model, and theory — clarifying how identification, non-causal explanation, and theoretical inference jointly support causal knowledge.