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Causality
is relationship between two variables such that one (the independent variable) can be
claimed to have caused the other (the dependent variable).
In order to establish causality three conditions must be met:
there must be a correlation or association between
variables;
the independent variable (the cause) must occur before the
dependent variable (the effect);
the relationship must not be spurious.
Causality in Social Network Analysis
PATRICK DOREIAN, University of Pittsburgh
Sociological Methods & Research, Vol. 30, No. 1, 81-114 (2001) DOI:
10.1177/0049124101030001005 Đ 2001 SAGE Publications
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. -
smr.sagepub.com/cgi/content/abstract/30/1/81
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
Grangerīs test, a modified version of Grangerīs test here suggested, TSLS, Hausmanīs
causality test and other approaches. The main conclusion is that the modified version of
Grangerīs test performs rather well and that Hausmanīs test is very often useful for
reinforcing the conclusions of multiple equations models with contemporaneous
interdependence. 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. -
ideas.repec.org/a/eaa/ijaeqs/v1y2004i1_6.html
An Action-Related Theory of Causality
Donald Gillies, Department of Philosophy, King's College London, Strand, London WC2R
2LS UK donald.gillies@kcl.ac.uk
bjps.oxfordjournals.org/cgi/content/abstract/56/4/823
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. Medical theories are closely connected with practical action (attempts to cure and
prevent disease), whereas theoretical physics is more remote from applications. This
suggests the view that causal laws are appropriate in a context where there is a close
connection to action. This leads to a development of an action-related theory of causality
which is similar to the agency theory of Menzies and Price, but differs from it in a
number of respects, one of which is the following. Menzies and Price connect ‘A
causes B’ with an action to produce B by instantiating A, but, particularly in the
case of medicine, the law can also be linked to the action of trying to avoid B by
ensuring that A is not instantiated. 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.
Introduction
Russell on causality
Preliminary exposition of the action-related theory
Differences between the action-related theory and the agency theory of Menzies and
Price
Explanation of causal asymmetry
Objections to the action-related theory
Extension of the theory to the indeterminate case
The Causality Between Corruption, Poverty and Growth: a Panel Data Analysis
By: Felix Fofana N’ZUE and Coffi Jose Francis N’GUESSAN
Publication: 2006 - unpan1.un.org
In: SISERA Working Paper Series
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 study aims specifically at: i) determining whether corruption causes growth or
vice-versa; ii) determining whether poverty causes growth or vice-versa; or iii) whether
it is the combine effect of corruption and poverty that causes growth. The link between
corruption, poverty and growth was analyzed in a panel of 18 African countries for the
1996-2001 time periods.
Indicators of poverty and corruption were identified and tests of the causal relationship
between these variables were conducted using panel data analysis. The empirical results
suggest that: 1) it is poverty that causes growth but not the other way around. This
implies that past information of the state of human development help improve prediction on
growth, 2) it is the state of growth that causes corruption and inequality; 3) It is
corruption that causes inequality; 4) corruption and poverty together cause growth, 5)
poverty and growth together cause corruption; 6) and lastly, inequality together with
growth cause corruption.
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 (usually the budget balance or expenditure growth) 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. -
200.32.4.58/~economia/summer/jaimovich.pdf |
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