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Causality is relationship between two variables such that one (independent variable) can be claimed to have caused the other (dependent variable). In order to establish causality there must be a correlation or association between variables, the independent variable (the cause) must occur before the dependent variable (the effect) and there must not be any spuriousness. A necessary condition is one that must be satisfied for the statement to be true. A sufficient condition is one that, if satisfied, guarantees the statement will be true. Some conditions can be both Necessary Condition and Sufficient Condition. Causality: Statistical Perspectives and Applications presents a wide-ranging collection of seminal contributions by renowned experts in the field, providing a thorough treatment of all aspects of statistical causality.
Time and Causality across the Sciences by Samantha Kleinberg brings together research on all facets of how time and causality relate across the sciences. Time is fundamental to how we perceive and reason about causes. It lets us immediately rule out the sound of a car crash as its cause. That a cause happens before its effect has been a core, and often unquestioned, part of how we describe causality. Across psychology, biology, and the social sciences, common themes emerge, suggesting that time plays a critical role in our understanding. The increasing availability of large time series datasets allows us to ask new questions about causality, necessitating new methods for modeling dynamic systems and incorporating mechanistic information into causal models.
Causality in Social Network Analysis - PATRICK DOREIAN, University of Pittsburgh. Sociological Methods & Research, Vol. 30, No. 1, (2001). The role that causality can play in social network analysis is unclear. The author provides a broad characterization of social network analysis before considering the nature of causality. He distinguishes four types of Causality: System Causality, Statistical Causality, Mechanism Causality, and Algorithmic Causality. Their potential places in network analysis are discussed. Understanding generative mechanisms, be they system, mechanism, or algorithmic, seems the most promising way to proceed. The role of statistical causality is a source of potential data analytic tools that can be mobilized within analyses conducted in the spirit of the other three types of causality.
The Causality Between Corruption, Poverty and Growth: a Panel Data Analysis. By: Felix Fofana NZUE and Coffi Jose Francis NGUESSAN. Abstract: The main purpose of this study was to shed more light on the links between corruption, poverty and growth based on the notion of causality in the context of panel data. The empirical evidence suggest that it is poverty that causes growth but not the other way around; It it is the state of growth that causes corruption and inequality; It is corruption that causes inequality; and inequality together with growth cause corruption.
A Comparison of Causality Tests Applied to the Bilateral Relationship between Consumption and GDP in the USA and Mexico - Guisan, M.Carmen, M. Carmen Guisan. Abstract: This article compares several methodologies for analysing unidirectional and bi-directional causality between Consumption and GDP in the USA, Mexico and other countries during the period 1960-2000. Bilateral causality is analysed comparing Granger's test, a modified version of Granger's test. Regarding the bilateral relationship between Consumption and GDP we conclude that there is a moderate degree of contemporaneous relation, with a high degree of dependence of Private Consumption on GDP and a lower dependence in the case of the reverse relation, because GDP is more dependent on supply side conditions than on demand side. This result is relevant for economic policies in less developed countries where very often emphasis is made more in the reverse relations than in the main ones.
An Action-Related Theory of
Donald Gillies, Department of Philosophy, King's College London. The paper begins with a discussion of Russell's view that the notion of cause is unnecessary for science and can therefore be eliminated. It is argued that this is true for theoretical physics but untrue for medicine, where the notion of cause plays a central role. This leads to a development of an action-related theory of causality which is similar to the agency theory of Menzies and Price. The action-related theory has in common with the agency theory of Menzies and Price the ability to explain causal asymmetry in a simple fashion, but the introduction of avoidance actions together with some ideas taken from Russell enable some of the objections to agency accounts of causality to be met.
Procyclicality or Reverse Causality?
Very Preliminary, Dany Jaimovich, Ugo Panizza. Abstract: There is a large literature showing that fiscal policy is either acyclical or countercyclical in industrial countries and procyclical in developing countries. Most of this literature is based on OLS regressions that focus on the correlation between a fiscal variable and either GDP growth or some measure of output gap. In this paper, we argue that this methodology does not allow to identify the causal effect of the business cycle on fiscal policy and hence cannot be used to estimate policy reaction functions. We propose a new instrument for GDP growth and show that, once GDP growth is properly instrumented, procyclicality tends to disappear.