of the Dissertation Generalizability in Causal Inference: Theory and Algorithms

For many years, meeting satisfaction has been a key outcome variable in experimental Group Support Systems (GSS) research. GSS research results on meeting satisfaction are conflicting, reporting positive, negative, or no effects. Unfortunately, no causal model of meeting satisfaction has been developed that could explain these effects. This paper derives Satisfaction Attainment Theory (SAT), a ...

We investigate how people use causal knowledge to design interventions to affect the outcomes of causal systems. We propose that in addition to using content or mechanism knowledge to evaluate the effectiveness of interventions, people are also influenced by the abstract structural properties of a causal system. In particular, we investigated two factors that influence whether people tend to in...

Scenario based requirements specifications are the industry norm for defining communication protocols. Basic scenarios captured as UML sequence diagrams, Message Sequence Charts (MSCs) or Live Sequence Charts (LSC) have partial order semantics that characterize system traces by restricting the possible order of events within those traces. The semantic partial order of the scenario specification...

BACKGROUND This study examined the effect of types of causal information about overweight on children's attitudes and intentions toward a peer presented as overweight. METHODS Participants (N = 176) were randomly assigned to read a vignette of an overweight peer in one of three conditions, which varied in the explanatory information provided for the aetiology of the peer's overweight conditio...

The mental model theory of naive causal understanding and reasoning (Goldvarg & Johnson-Laird, 2001, Cognitive Science, 25, 565-610) claims that people distinguish between causes and enabling conditions on the basis of sets of models that represent possible causal situations. In the tasks used to test this hypothesis, however, the proposed set of models was confounded with linguistic cues that ...

For nearly as long as the word " correlation " has been part of statistical parlance, students have been warned that correlation does not prove causation, and that only experimental studies, e.g., randomized clinical trials, can establish the existence of a causal relationship. Over the last few decades, somewhat of a consensus has emerged between statisticians, computer scientists, and philoso...

This paper concerns the empirical basis of causation, and addresses the following issues: 1. the clues that might prompt people to perceive causal relationships in uncontrolled observations. 2. the task of inferring causal models from these clues, and 3. whether the models inferred tell us anything useful about the causal mechanisms that underly the observations. We propose a minimal-model sema...

Carl Craver’s mutual manipulability criterion aims to pick out all and only those components of a mechanism that are constitutively relevant with respect to a given phenomenon. In devising his criterion, Craver has made heavy use of the notion of an ideal intervention, which is a tool for illuminating causal concepts in causal models. The problem is that typical mechanistic models contain non-c...

We present a probabilistic theory of causal explanations, which integrates probabilistic and causal knowledge. Unlike most other approaches where a causal explanation is a hypothesis that one or more causative events occurred , we deene an explanation of a set of observations to be the occurrence of a chain of causation events. These causation events constitute a scenario where all the observat...