Available in Synthese: https://rdcu.be/b7VNz
Conditional learning, where agents learn a conditional sentence 'If A, then B,' is difficult to incorporate into existing Bayesian models of learning. This is because conditional learning is not uniform: in some cases, learning a conditional requires decreasing the probability of the antecedent, while in other cases, the antecedent probability stays constant or increases. I argue that how one learns a conditional depends on the causal structure relating the antecedent and the consequent, leading to a causal model of conditional learning. This model extends traditional Bayesian learning by incorporating causal models into agents' epistemic states. On this theory, conditional learning proceeds in two steps. First, an agent learns a new causal model with the appropriate relationship between the antecedent and the consequent. Then, the agent narrows down the set of possible worlds to include only those which make the conditional proposition true. This model of learning can incorporate both standard cases of Bayesian learning and the non-uniform learning required to learn conditional information.
Available on PhilPapers: https://philpapers.org/archive/VANCMA-6.pdf
Causal models provide a framework for making counterfactual predictions, making them useful for evaluating the truth conditions of counterfactual sentences. However, current causal models for counterfactual semantics face limitations compared to the alternative similarity-based approach: they only apply to a limited subset of counterfactuals and the connection to counterfactual logic is not straightforward. This paper argues that these limitations arise from the theory of interventions where intervening on variables requires changing structural equations rather than the values of variables. Using an alternative theory of exogenous interventions, this paper extends the causal approach to counterfactuals to handle more complex counterfactuals, including backtracking counterfactuals and those with logically complex antecedents. The theory also validates familiar principles of counterfactual logic and offers an explanation for counterfactual disagreement and backtracking readings of forward counterfactuals.
Available on PhilPapers: https://philpapers.org/archive/VANCMA-7.pdf
Safety purports to explain why cases of accidentally true belief are not knowledge, addressing Gettier cases and cases of belief based on statistical evidence. However, numerous examples suggest that safety fails as a condition on knowledge: a belief can be safe even when one's evidence is clearly insufficient for knowledge and knowledge is compatible with the nearby possibility of error, a situation ruled out by the safety condition. In this paper, I argue for a new modal condition designed to capture the non-accidental relationship between facts and evidence required for knowledge: causal safety. I argue that possible errors in belief can be captured by accounting for deviations in causal relationships and that there is a natural way to characterize which causal errors are relevant in an epistemic situation. Using this, I develop a causal analogue to safety, where one's belief in $p$ is causally safe iff it is true in all causally relevant worlds where one believes $p$. Causal safety, I argue, can explain the cases safety is meant to address while avoiding the issues raised for safety.
Available on PhilPapers: https://philpapers.org/archive/VANTRC-4.pdf
Conditional probability is often used to represent the probability of the conditional. However, triviality results suggest that the thesis that the probability of the conditional always equals conditional probability leads to untenable conclusions. In this paper, I offer an interpretation of this thesis in a possible worlds framework, arguing that the triviality results make assumptions at odds with the use of conditional probability. I argue that these assumptions come from a theory called the operator theory and that the rival restrictor theory can avoid these problematic assumptions. In doing so, I argue that recent extensions of the triviality arguments to restrictor conditionals fail, making assumptions which are only justified on the operator theory.
Available on PhilPapers: https://philpapers.org/archive/VANLAH-5.pdf
The history of science is often conceptualized through 'paradigm shifts,' where the accumulation of evidence leads to abrupt changes in scientific theories. Experimental evidence suggests that this kind of hypothesis revision occurs in more mundane circumstances, such as when children learn concepts and when adults engage in strategic behavior. In this paper, I argue that the model of hypothesis testing can explain how people learn certain complex, theory-laden propositions such as conditional sentences ('If A, then B') and probabilistic constraints ('The probability that A is p'). Theories are formalized as probability distributions over a set of possible outcomes and theory change is triggered by a constraint which is incompatible with the initial theory. This leads agents to consult a higher order probability function, or a 'prior over priors,' to choose the most likely alternative theory which satisfies the constraint. The hypothesis testing model is applied to three examples: a simple probabilistic constraint involving coin bias, the sundowners problem for conditional learning, and the Judy Benjamin problem for learning conditional probability constraints. The model of hypothesis testing is contrasted with the more conservative learning theory of relative information minimization, which dominates current approaches to learning conditional and probabilistic information.