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...

The Markov condition describes the conditional independence relations present in a causal model that are consequent to its graphical structure, whereas the faithfulness assumption presumes that there are no other independencies in the model. Cartwright argues that causal inference methods have limited applicability because the Markov condition cannot always be applied to domains, and gives an e...

An important goal in both transfer learning and causal inference is to make accurate predictions when the distribution of the test set and the training set(s) differ. Such a distribution shift may happen as a result of an external intervention on the data generating process, causing certain aspects of the distribution to change, and others to remain invariant. We consider a class of causal tran...

Typically, in the practice of causal inference from observational studies, a parametric model is assumed for the joint population density of potential outcomes and treatment assignments, and possibly this is accompanied by the assumption of no hidden bias. However, both assumptions are questionable for real data, the accuracy of causal inference is compromised when the data violates either assu...

Social epidemiology is the study of relations between social factors and health status in populations. Although recent decades have witnessed a rapid development of this research program in scope and sophistication, causal inference has proven to be a persistent dilemma due to the natural assignment of exposure level based on unmeasured attributes of individuals, which may lead to substantial c...

The manipulationist account of causation provides a conceptual analysis of cause-effect relationships in terms of hypothetical experiments. It also explains why and how experiments are used for the empirical testing of causal claims. This paper attempts to apply the manipulationist account of causation to a broader range of experiments—a range that extends beyond experiments explicitly designed...

Motivation – This paper describes the initial results of a naturalistic inquiry into the way people derive causal inferences. Research approach – We examined media accounts of economic, political, military, and sports incidents to determine the types of causal explanations that are commonly invoked. Findings – We found two interacting processes at work: the identification of potential causes an...

The concepts of information transfer and causal effect have received much recent attention, yet often the two are not appropriately distinguished and certain measures have been suggested to be suitable for both. We discuss two existing measures, transfer entropy and information flow, which can be used separately to quantify information transfer and causal information flow respectively. We apply...

This paper investigates a solution to the problem of causal ordering in message passing distributed systems Causal ordering is the restriction that messages are delivered in a fo fashion with respect to the global causal order between events in the system This is stronger than the condition that each local channel is fo In our algorithm causal ordering is implemented by having each process main...

The discovery of conjunctive causes--factors that act in concert to produce or prevent an effect--has been explained by purely covariational theories. Such theories assume that concomitant variations in observable events directly license causal inferences, without postulating the existence of unobservable causal relations. This article discusses problems with these theories, proposes a causal-p...