Bayesian Causal Modeling in Social Sciences
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Bayesian Causal Modeling in Social Sciences
- 1. Bayésianisme objectif et modélisation causale en sciences sociales Federica Russo Philosophie, Louvain & Kent IHPST – 5 d é cembre 2008
- 10. Bayesianism empirically-based or objective is the interpretation that best fit CM
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- For instance, if A is the proposition that a coin tossed at time t will fall heads, X is the proposition that the chance of A at time t is x , and E is the available evidence that does not contradict X ; the Principal Principle then says that x equals the actual degree of belief that the coin falls heads, conditionally on the proposition that its chance of falling heads is x . In other words, the chance of A equals the degree to which an agent believes in A .
- In fully personalistic approaches coherence is nec and suff for assignments of prior proba. So, the objection: aren’t frequencies just a pedagogical need? Carnap gives an interesting answer (Philosophical Foundations of Probability, §50-51, 41C). 1. we can do without frequencies if inductive logic is accepted. Reason 1: prob1 (subj) can be explicated as estimate of proba2 (obj). So, if proba2 is know, then proba1 just equals this value. Reason 2: even if proba2 is unknown, we can still compute proba1 as estimate of the unknown proba2 from *frequencies* in the sample But the subjectivist can still play a last card: the exchangeability argument. Briefly and informally, De Finetti shows that different agents may start with different prior probabilities, but, as evidence accumulates, their posterior proba will tend to converge, thus giving the illusory impression that objective probability exists.