Instrumental variables (IVs) are widely used to identify causal effects. For this purpose IVs have to be exogenous, i.e., causally unrelated to all variables in the model except the explanatory variable X . It can be hard to find such variables. A generalized IV method has been proposed that only requires exogeneity conditional on a set of covariates. This leads to a wider choice of potential I...

STATISTICAL ANALYSIS IN EMPIRICAL BAYES AND IN CAUSAL INFERENCE

of the Dissertation Complete Identification Methods for Causal Inference

The paper addresses uncertain reasoning on a causal model given by two layered networks, where nodes in one layer express possible causes and those in the other are possible e$ects. Uncertainty of causalities is expressed in a novel manner, i.e. by Conditional Causal Possibilities. The expression has two advantages over the conventional way with conditional possibilities: it expresses the exact...

The central aims of this experiment were to compare observational and interventional learning of a simple causal chain, and to ascertain whether people represent their interventions in accordance with the normative model proposed by Pearl (2000). In the observation condition people treated putative causes as independent, and systematically selected the wrong model. In the intervention condition...

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...

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...

The micro-macro paradox has been revived. Despite broadly positive evaluations at the micro and meso-levels, recent literature has turned decidedly pessimistic with respect to the ability of foreign aid to foster economic growth. Policy implications, such as the complete cessation of aid to Africa, are being drawn on the basis of fragile evidence. This paper first assesses the aid-growth litera...