Causal conditional reasoning means drawing inferences from a conditional statement that refers to causal content. It is argued that data on causal conditional reasoning not only tell us something about how people draw deductive inferences from conditionals, but also provide us with information about how they understand causal relations. In particular, three principles emerge from existing data:...

This article traces the philosophical and psychological connections between causation and the conditional, if...then, across the two main paradigms used in conditional reasoning, the selection task and the conditional inference paradigm. It is argued that hypothesis testing in the selection task reflects the philosophical problems identified by Quine and Goodman for the material conditional int...

The paper addresses uncertain reasoning based on causal knowledge given by two layered networks, where nodes in one layer express possible causes and those in the other are possible e/ects. Uncertainties of the causalities are given by conditional causal possibilities, which were proposed to express the exact degrees of possibility of causalities. The expression of the uncertainty also has an a...

People can often outperform statistical methods and machine learning algorithms in situations that involve making inferences about the relationship between causes and effects. While people are remarkably good at causal reasoning in many situations, there are several instances where they deviate from expected responses. This paper examines three situations where judgments related to causal infer...

Conditional Causal Probability (CCPR) and Conditional Causal Possibility (CCPO) have been proposed to express exact uncertainties of causalities, and some reasoning methods based on them have been studied to calculate probabilities or possibilities of unknown events under the condition that some events are known. CCPR/CCPO is a conditional probability/possibility of a causation event conditione...

This paper addresses an uncertain reasoning based on causal knowledge given by two layered network, where nodes in one layer express possible causes and those in the other are possible results. Uncertainties of the causalities are given by Conditional Causal Possibilities, which are proposed to express uncertainties of causalities we recognize in mind. The conventional Conditional Possibilities...

This paper considers the problem of inferring a discrete joint distribution from a sample subject to selection. Abstractly, we want to identify a distribution p(x,w) from its conditional p(x |w). We introduce new assumptions on the marginal model for p(x), under which generic identification is possible. These assumptions are quite general and can easily be tested; they do not require precise ba...

In many causal inference problems, one is interested in the direct causal effect of an exposure on an outcome of interest that is not mediated by certain intermediate variables. Robins and Greenland (1992) and Pearl (2000) formalized the definition of two types of direct effects (natural and controlled) under the counterfactual framework. Since then, identifiability conditions for these effects...