Counterfactual and Semifactual Conditionals Prime Alternative Possibilities.

The authors examined in 3 experiments the comprehension of counterfactuals, such as "If it had rained, the plants would have bloomed," and semifactuals, such as "Even if it had rained, the plants would have bloomed," compared with indicative conditionals, "If it rained, the plants bloomed." The first experiment showed that people read the negative conjunction, "not p and not q" faster when it was primed by a counterfactual than when it was primed by an indicative conditional. They read the affirmative conjunction, "p and q" equally quickly when it was primed by either conditional. The 2nd experiment showed that people read the negated-antecedent conjunction, "not p and q" faster when it was primed by a semifactual conditional. The 3rd experiment corroborated these results in a direct comparison of counterfactuals and semifactuals. The authors discuss the implications of the results for the mental representations of different conditionals. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Counterfactuals and Updates as Inverse Modalities

We point out a simple but hitherto ignored link between the theoryof updates, the theory of counterfactuals, and classical modal logic: update is a classicalexistential modality, counterfactual is a classical universalmodality, and the accessibility relations corresponding to these modalities are inverses. The Ramsey Rule (often thought esoteric) is simply an axiomatisation of this inverse relationship. We use this fact to translate between rules for updates andrules for counterfactuals. Thus, Katsuno and Mendelzons postulatesU1--U8 are translated into counterfactual rules C1--C8(Table VII), and many of the familiar counterfactual rulesare translated into rules for updates (Table VIII). Ourconclusions are summarised in Table V.From known properties of inverse modalities we deduce that notall rules for updates may be translatedinto rules for counterfactuals, and vice versa. We present asyntactic condition which is sufficient to guarantee that atranslation from update to counterfactual (or vice versa) is possible.     

Counterfactual and Prefactual Conditionals.

We consider reasoning about prefactual possibilities in the future, for example, "if I were to win the lottery next year I would buy a yacht" and counterfactual possibilities, for example, "if I had won the lottery last year, I would have bought a yacht." People may reason about indicative conditionals, for example, "if I won the lottery I bought a yacht" by keeping in mind a few true possibilities, for example, "I won the lottery and I bought a yacht." They understand counterfactuals by keeping in mind two possibilities, the conjecture, "I won the lottery and I bought a yacht" and the presupposed facts, "I did not win the lottery and I did not buy a yacht." We report the results of three experiments on prefactuals that examine what people judge them to imply, the possibilities they judge to be consistent with them, and the inferences they judge to follow from them. The results show that reasoners keep a single possibility in mind to understand a prefactual. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Causality and Counterfactuals in the Situation Calculus

Structural causal models offer a popular framework for exploringcausal concepts. However, due to their limited expressiveness,structural models have difficulties coping with such conceptsas actual (event-to-event) causation. In this article, we proposea new type of causal model, based on embedding structural considerationsin the language of situation calculus. By using situation calculusas a basic language, we leverage its power to express complex,dynamically changing situations and, by relying on structuralconsiderations, we can formulate an effective theory of counterfactualswithin the situation-calculus.     

Thinking within the box: The relational processing style elicited by counterfactual mind-sets.

By comparing reality to what might have been, counterfactuals promote a relational processing style characterized by a tendency to consider relationships and associations among a set of stimuli. As such, counterfactual mind-sets were expected to improve performance on tasks involving the consideration of relationships and associations but to impair performance on tasks requiring novel ideas that are uninfluenced by salient associations. The authors conducted several experiments to test this hypothesis. In Experiments 1a and 1b, the authors determined that counterfactual mind-sets increase mental states and preferences for thinking styles consistent with relational thought. Experiment 2 demonstrated a facilitative effect of counterfactual mind-sets on an analytic task involving logical relationships; Experiments 3 and 4 demonstrated that counterfactual mind-sets structure thought and imagination around salient associations and therefore impaired performance on creative generation tasks. In Experiment 5, the authors demonstrated that the detrimental effect of counterfactual mind-sets is limited to creative tasks involving novel idea generation; in a creative association task involving the consideration of relationships between task stimuli, counterfactual mind-sets improved performance. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Statistics and causal inference: A review

This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, the assessment of causal effects, the interpretation of counterfactuals, and a symbiosis between counterfactual and graphical methods of analysis. Preliminary version of this paper was published inHealth Services and Outcomes Research Methodology, 2:189–220, 2001.