Page 37 - DLIS401_METHODOLOGY_OF_RESEARCH_AND_STATISTICAL_TECHNIQUES

Methodology of Research and Statistical Techniques

Notes 2.1.3 Causal Modelling

1. Assumptions of Causal Inquiry
The first step in causal modelling involves conceptualization: what are the relevant concepts,
and, second, how to operationalize these concepts. The next step is formalization, i.e. specification
of the relationships between the variables. This seems to destroy the richness of the theory, but
it helps to achieve comprehensibility and avoids logical inconsistencies. Note that this model
is ideally based on a deductive approach, but it does not exclude a more dynamic approach
which moves back and forth (from theory to data).

The causal model itself specifies not only the direction (from X to Y) but also the sign of the
relationship (positive or negative). A positive relationship means that when X goes up, Y goes
up; a negative relationship between X and Y means that as X goes up Y goes down between
different paths, the signs should be multiplied to determine the net-effect. A causal system is
consistent when all the causal chains push the relationship in the same direction (indicated by
the fact that all the signs are the same). When some signs are positive, others negative, the
system is inconsistent (suppressors).
Please note that the causality is not in reality (perhaps it is), but it is above all put into the
model by virtue of the theory. This involves a notion of determinism (for the sake of the
model), and that we stop some place in looking for any more causes or effects. Also note that
the variables in a causal model are all at the same level of abstraction (ideally).
Causal explanations can be idiographic or nomothetic— (1) idiographic explanations seek to
explain a particular events in terms of all its caused (deterministic model); (2) nomothetic
explanations seek to explain general classes of actions or events in terms of the most important
causes (probabilistic model).
2. Causal Order: Definitions and Logic
Prior (unknown or not considered) variables precede the independent variable. Intervening
variables are located in between the independent and dependent variable. Consequent variables
are all variables coming after the dependent variable (unknown or not considered). Note that
the identification of prior, independent, intervening, dependent, and consequent variables is
relative to the model at hand. The causal order between a number of variables is determined
by assumptions that determine the causal system that determines the relationship between
those variables. (note that variables in a loop have no order, i.e., when the path from X away
to other variables returns from those variables back to X).
The following possibilities can be distinguished:

- X causes Y
- X and Y influence eachother
- X and Y correlate
Variable X causes variable Y, when change in X lead to change in Y, or when fixed attributes
of X are associated with certain attributes of Y. This implies, of course, that we talk about
certain tendencies: X is a (and not the) cause. And this implies correlation as a minimum,
necessary condition (the causation itself is theoretical).
3. Minimum-Criteria for Causality

Rule 1: Covariation
Two variables must be empirically correlated with one another, they must co-vary, or one of
them cannot have caused the other. This leads to distinguish direct from indirect effects.

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