1. Introduction to Area Studies Causal Analysis Presenter – Sue Nah RHIE

2. Causal Analysis - Introduction  CA is one of several futures techniques used as a means to inquire into the causes of social phenomena and to generate a set of forecasts as to the future course of phenomena.  It seeks to integrate empiricist, interpretive, critical and action learning modes of knowing.  It is not in predicting the future but in creating transformative spaces for the creation of alternative futures.  It develops a more effective, deeper, inclusive, longer term policy.

3. Four Levels of Analysis 1. The Litany – the official unquestioned view of reality 2. The Social Causation level, the systemic perspective. The data of the litany is explained and questioned at this level. 3. The Worldview/discourse. Deeper, unconsciously held ideological, worldview and discursive assumptions are unpacked at this level. The way in which different stakeholders construct the litany and system are also explored. 4. The Myth-metaphor, the unconscious emotive dimensions of the issue. The challenge is to conduct research that moves up and down these layers of analysis and thus is inclusive of different ways of knowing. Doing so also allows for the creation of authentic futures and integrated transformation.

4. Causal Analysis – Introduction (cont.)  Different Sorts of Causal Claims  Singular causal judgments  Generic causal relations  Causal relevance claims  Probabilistic causal claims  Wide variety of factors function as either CAUSE or EFFECT in social analysis  E.g..) individual actions, collective actions, social structures, state activity, forms of organization, systems of norms and values, cultural modes of representation, social relations, and geographic and ecological features of an environment

5. Causal Analysis – Introduction (cont.)  Social explanations depend on causal reasoning, with qualifications 1)The causal assertions that are put forward within social science usually do not depend on a simple inductive generalization. 2)These claims typically do depend on an analysis of the specific causal mechanisms that connect cause and effect. 3)The mechanisms that social causal explanations postulate generally involve reference to the beliefs and wants, powers and constraints that characterize the individuals whose actions influence the social phenomenon.

6. Inductive vs. Deductive  Inductive  The process of reasoning in which in which the premises of an argument are believed to support the conclusion but do not ensure it.  E.g.. All fruits comes from flowers.  Mugunghwa is a flower  So, Mugunghwa should have a fruit.  Deductive  The process of reasoning in which the conclusion is necessitated by, or reached from, previously known facts. If the premises are true, the conclusion must be true.  E.g.. All people die some day.  Bush is a person  So, Bush may die some day.

7. Causal Reasoning  Three Central Ideas  The idea of causal mechanism connecting cause and effect (CM)  most fundamental idea  C is a cause of E: a series of events of C leading C to E  The idea of a correlation between two or more variables, the inductive regularity (IR)  C is a cause of E: there is a regular association between C- type events and E-type events  The idea that one event is a necessary or sufficient condition for another (NSC)  C is a cause of E: C is a necessary and/or sufficient condition for the occurrence of E

8. Causal Mechanism  A series of events connecting C and E  This series of events C 1 constitutes the causal mechanism linking C to E, and the laws that govern transitions among the events C 1 are the causal laws determining the causal relation between C and E.  In this events are causally related if and only if there are causal laws that lead from cause to effect.  A series of events governed by law like regularities that lead from the explanans to the explanandum.

9. Causal Mechanism (cont.)  Connection with causal reasoning and social phenomena  The mechanisms that link cause and effect are typically grounded in the meaningful, intentional behavior of individuals.  Difference between social science and natural science  Social phenomena are constituted by individuals whose behavior is the result of their rational decision-making and non-rational psychological processes that sometimes are at work

10. The Inductive-Regularity Criterion  The general idea of IR is that causal relations consist only in patterns of regular association between variables, classes of events, and the like.  For example, a pair of variables, C and E are causally related if and only if there is a regularity conjoining events of type C and events of type E.  This association between discrete variables E and C can be expressed in terms of conditional probabilities : E is associated with C if and only if the conditional probability of E given C is different from the absolute probability of E.  It is necessary to construct a hypothesis about the causal mechanisms that connect these variables.

11. The Inductive-Regularity Criterion - Problems  There is a problem of a spurious correlation between variables  The presence of a regularity between two variables does not establish a causal link between them.  False-positive error: It classifies a relation between two variables as causal when in fact it is not.  False-negative Errors: conclusions that there is no causal relation between two variables when in fact there is.

12. Relation between CM and IR  The Inductive Regularity Criterion is secondary to the Casual Mechanism Criterion.  Causal relation only exists when there is a causal mechanism connecting them between two variables.  Although facts about inductive regularities are useful for identifying possible causal relations, investigation of underlying causal processes is necessary before we can conclude that a relation exists.  Therefore, IR criterion should be understood as a source of causal hypotheses and a method to evaluate them empirically – not as a definition of causation.

13. Necessary and Sufficient Conditions  To say that P is necessary and sufficient for Q is to say two things:  P is necessary for Q (Q  P)  P is sufficient for Q (P  Q)  E.g. 1)  If Sue always studies in the library on Monday, but never on any other day, it can be said "being Monday is a necessary condition for Sue to study in the library." This is so since Sue does not study in the library on days that are not Monday. Also, "being Monday is a sufficient condition for Sue to study in the library." This is true since Sue always studies in the library on Monday.

14. Necessary and Sufficient Conditions  E.g. 2)  Consider the thunder/lightning. "Thunder is necessary for lightning", since absolutely no thunder means there isn't any lightning to create any noise. "Thunder is sufficient for lightning" since thunder (which we have defined narrowly) must have originated from some lightning.  E.g. 3)  The relationship between being a square and being a rectangle is one which is NOT "necessary and sufficient." "Being a rectangle is necessary for being a square", yet "being a rectangle is NOT sufficient for being a square". "Being a square is sufficient for being a rectangle", yet "being a square is NOT necessary for being a rectangle."  "P is necessary and sufficient for Q" expresses the same thing as "P if and only if Q" (P  Q).

15. Forms of Causal Reasoning  The Case-study Method  A common approach to a causal question, in which the investigator examines the history of the event in detail to arrive at a set of causal hypotheses about its course.  A detailed study of a particular sequence of social events and processes.  Depends on identifying particular causal links among historical events and circumstances.  A theoretical or inductive basis for asserting that a given historical circumstance affected the occurrence and character of a subsequent circumstance is needed

16. The Case-study Method  To identify casual mechanisms 1) A deductive approach ; establishing casual connections between social factors based on a theory of the underlying processes 2) A broadly inductive approach ; justifying the claim that A 1 caused B 1 on the ground that events of type A are commonly associated with events of type B

17. The Comparative Method  The comparative method of cases that embody a range of similar characteristics with certain salient differences.  In the comparative approach the investigator identifies a small number of cases in which the phenomenon of interest occurs in varying degrees and then attempts to isolate the causal processes that lead to different outcomes.  Focuses on the details of a few cases in order to probe the mechanisms of change, the details of the processes, and the presence or absence of specific factors.

18. Mill’s Method  John Stuart Mill, System of Logic  The methods of agreement and difference; it is aimed at identifying the cause of an event by observing variations in antecedent conditions for repeated occurrences of the event.  Problems: it cannot handle complex causation and probabilistic causation.

19. Things to keep in mind in General  Consider the available data and supporting evidence  Re-examine our material for possible alternative causes and effects.  Avoid fallacy  Offer evidence to support what might otherwise be merely an assertion of a cause relationship. Where appropriate, use facts and figures to convince our readers of the validity of our causal analysis. Statistics and the testimony of experts will often more readily convince than to accept our analysis.

20. Conclusion  Fundamental Idea  Causal Mechanism  To assert that E was caused by C means that there is “ a causal mechanism leading from the occurrence of C to the occurrence of E.”  Two other prominent ideas  causal judgments correspond to:  inductive regularities  and express claims about necessary and sufficient conditions.