Conditionals Right and Left: Probabilities for the Whole Family

The fact that the standard probabilistic calculus does not define probabilities for sentences with embedded conditionals is a fundamental problem for the probabilistic theory of conditionals. Several authors have explored ways to assign probabilities to such sentences, but those proposals have come under criticism for making counterintuitive predictions. This paper examines the source of the problematic predictions and proposes an amendment which corrects them in a principled way. The account brings intuitions about counterfactual conditionals to bear on the interpretation of indicatives and relies on the notion of causal (in)dependence.     

Structural equations and causation: six counterexamples

Hall [(2007), Philosophical Studies, 132, 109–136] offers a critique of structural equations accounts of actual causation, and then offers a new theory of his own. In this paper, I respond to Hall’s critique, and present some counterexamples to his new theory. These counterexamples are then diagnosed.

From a necessary being to god

Not a lot of work on theistic arguments has been devoted to drawing connections between a necessary being and theistic properties. In this paper, I identify novel paths from a necessary being to certain theistic properties: volition, infinite power, infinite knowledge, and infinite goodness. The steps in those paths are an outline for future work on what William Rowe (The Cosmological Argument, 1975, p. 6) has called “stage II” of the cosmological argument.     

Are Newcomb problems really decisions?

Richard Jeffrey long held that decision theory should be formulated without recourse to explicitly causal notions. Newcomb problems stand out as putative counterexamples to this ‘evidential’ decision theory. Jeffrey initially sought to defuse Newcomb problems via recourse to the doctrine of ratificationism, but later came to see this as problematic. We will see that Jeffrey’s worries about ratificationism were not compelling, but that valid ratificationist arguments implicitly presuppose causal decision theory. In later work, Jeffrey argued that Newcomb problems are not decisions at all because agents who face them possess so much evidence about correlations between their actions and states of the world that they are unable to regard their deliberate choices as causes of outcomes, and so cannot see themselves as making free choices. Jeffrey’s reasoning goes wrong because it fails to recognize that an agent’s beliefs about her immediately available acts are so closely tied to the immediate causes of these actions that she can create evidence that outweighs any antecedent correlations between acts and states. Once we recognize that deliberating agents are free to believe what they want about their own actions, it will be clear that Newcomb problems are indeed counterexamples to evidential decision theory.     

Back to the future: Temporal perspective in the explanation of events

Prospective hindsight involves generating an explanation for a future event as if it had already happened; i.e., one goes forward in time, and then looks back. In order to examine how shifts in perspective might influence people's perceptions of events, we investigated two possible factors: temporal perspective (whether an event is set in the future or past) and uncertainty (whether the event's occurrence is certain or uncertain). In the first experiment, temporal perspective showed little influence while outcome uncertainty strongly affected the nature of explanations for events. Explanations for sure events tended to be longer, to contain a higher proportion of episodic reasons, and to be expressed in past tense. Evidence from the second experiment supports the view that uncertainty mediates not the amount of time spent explaining, but rather subjects' choice of explanation type. The implications of these findings for the use of temporal perspective in decision aiding are discussed.     

Who caused it? Interpersonal causal inferences in young children

Three experiments were designed to test 4- and 6-year-old children's causal inferences in interpersonal settings where emotions (glad, angry, and sad) were effect responses. The results showed that emotion and orientation (towards or away from) were central cues, and that sex and age also were used to some extent. Cues related to regularity philosophic notions (e.g. David Hume), such as contiguity in time and space, and time order of cause and effect were little used by comparison. The results raise questions about the basic role attributed to regularity cues both by philosophers and psychologists, and suggest a multiple cue contribution rather than a basic cue generalization approach to causal cognition development.     

Effect analysis and causation in linear structural equation models

This paper considers total and direct effects in linear structural equation models. Adopting a causal perspective that is implicit in much of the literature on the subject, the paper concludes that in many instances the effects do not admit the interpretations imparted in the literature. Drawing a distinction between concomitants and factors, the paper concludes that a concomitant has neither total nor direct effects on other variables. When a variable is a factor and one or more intervening variables are concomitants, the notion of a direct effect is not causally meaningful. Even when the notion of a direct effect is meaningful, the usual estimate of this quantity may be inappropriate. The total effect is usually interpreted as an equilibrium multiplier. In the case where there are simultaneity relations among the dependent variables in tghe model, the results in the literature for the total effects of dependent variables on other dependent variables are not equilibrium multipliers, and thus, the usual interpretation is incorrect. To remedy some of these deficiencies, a new effect, the total effect of a factorX on an outcomeY, holding a set of variablesF constant, is defined. When defined, the total and direct effects are a special case of this new effect, and the total effect of a dependent variable on a dependent variable is an equilibrium multiplier.For helpful comments, I am grateful to G. Arminger, K. Bollen, W. Faris, R. m. Hauser, T. Petersen, three anonymous Psychometrikas reviewers, and the Editor. For computational assistance, I am grateful to B. D. Kim.     

Learning to infer the time of our actions and decisions from their consequences

Research shows that people infer the time of their actions and decisions from their consequences. We asked how people know how much time to subtract from consequences in order to infer their actions and decisions. They could either subtract a fixed, default, time from consequences, or learn from experience how much time to subtract in each situation. In two experiments, participants’ actions were followed by a tone, which was presented either immediately or after a delay. In Experiment 1, participants estimated the time of their actions; in Experiment 2, the time of their decisions to act. Both actions and decisions were judged to occur sooner or later as a function of whether consequences were immediate or delayed. Estimations tended to be shifted toward their consequences, but in some cases they were shifted away from them. Most importantly, in all cases participants learned progressively to adjust their estimations with experience.     

Summarizing information by means of causal sentences through causal graphs

The objective of this work is to propose a complete system able to extract causal sentences from a set of text documents, select the causal sentences contained, create a causal graph in base to a given concept using as source these causal sentences, and finally produce a text summary gathering all the information connected by means of this causal graph. This procedure has three main steps. The first one is focused in the extraction, filtering and selection of those causal sentences that could have relevant information for the system. The second one is focused on the composition of a suitable causal graph, removing redundant information and solving ambiguity problems. The third step is a procedure able to read the causal graph to compose a suitable answer to a proposed causal question by summarizing the information contained in it.