causal models

Diagnostic causal reasoning with verbal information

In diagnostic causal reasoning, the goal is to infer the probability of causes from one or multiple observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of the …

From causal models to sound heuristic inference

We investigate whether people rely on their causal intuitions to determine the predictive value or importance of cues. Our real-world data set consists of one criterion variable (child mortality) and nine cues (e.g., GDP per capita). We elicited …

Structure induction in diagnostic causal reasoning

Our research examines the normative and descriptive adequacy of alternative computational models of diagnostic reasoning from single effects to single causes. Many theories of diagnostic reasoning are based on the normative assumption that inferences …

Repeated causal decision making

Many of our decisions refer to actions that have a causal impact on the external environment. Such actions may not only allow for the mere learning of expected values or utilities but also for acquiring knowledge about the causal structure of our …

Sequential diagnostic reasoning with verbal information

In sequential diagnostic reasoning, the goal is to infer the probability of a cause event from sequentially observed effects. Typically, studies investigating such tasks provide subjects with precise quantitative information regarding the strength of …

How causal reasoning can bias empirical evidence

Theories of causal reasoning and learning often implicitly assume that the structural implications of causal models and empirical evidence are consistent. However, for probabilistic causal relations this may not be the case. We propose a causal …

Spontaneous causal learning while controlling a dynamic system

When dealing with a dynamic causal system people may employ a variety of different strategies. One of these strategies is causal learning, that is, learning about the causal structure and parameters of the system acted upon. In two experiments we …

The tight coupling between category and causal learning

The main goal of the present research was to demonstrate the interaction between category and causal induction in causal model learning. We used a two-phase learning procedure in which learners were presented with learning input referring to two …

A rational model of elemental diagnostic inference

Whereas the traditional normative benchmark for diagnostic reasoning from effects to causes is provided by purely statistical norms, we here approach the task from the perspective of rational causal inference. The core feature of the presented model …

A transitivity heuristic of probabilistic causal reasoning

In deterministic causal chains the relations "A causes B" and "B causes C" imply that "A causes C". However, this is not necessarily the case for probabilistic causal relationships: A may probabilistically cause B, and B may probabilistically cause …