Chapter 10 Lecture Notes

1. Chapter 10 Lecture Notes Causal Inductive Arguments

2. Chapter 10 A causal inductive argument is an inductive argument in which the conclusion is a claim that one thing causes another. (286) For example: • Clogged arteries cause heart attacks • A rough surface produces friction • Exercise during heat causes sweating Causal inferences are often an attempt to explain or predict an outcome.

3. Chapter 10 Cogent arguments in support of causal claims are difficult to come by, and we should take great care when dealing with causal premises. There are lots of ways to express a cause in English without using the word ‘cause’ and here are just a few ways from pages 286-7. C produced E C was responsible for E C brought about E C led to E C created E C affected E C influenced E As a result of A, E occurred E was determined by C E was the result of C E was induced by C E was an effect of C

4. Chapter 10 The meaning of cause can vary as well because of the different nature of causal relationships. Here are four possible meanings for cause: • As a necessary condition • As a sufficient condition • As both a necessary and sufficient condition • As a contributory factor (neither necessary or sufficient) Each of these meanings of ‘cause’ are useful in analyzing arguments and premises in argument. We will see examples of each.

5. Chapter 10 Correlation vs. Causation Correlation is a symmetrical relationship, which causation is asymmetrical. Here are the three ways we might classify correlation relationships. • Positive correlation: if a higher proportion of Qs than non-Qs are H, then there is a positive correlation between being Q and being H. • Negative correlation: if a smaller proportion of Qs than non-Qs are H, then there is a negative correlation between being Q and being H. • No correlation: if about the same proportion of Qs as non-Qs are H, then there is no correlation between being Q and being H. A significant correlation is one that is reliable. (288)

6. Chapter 10 When Q is positively correlated with H, then one of the following has to be true, but it doesn’t have to be a causal relationship. • Q is a cause of H. • H is a cause of Q. • The positive correlation of Q and H is a coincidence. • Some other factor, X, is a cause of both Q and H. It should be clear now, why we cannot conclude that correlation implies causation given these four possibilities.

7. Chapter 10 Associations and Links To say that two things: A and B are linked is to claim that more than just a correlation between A and B exists. Linking suggests that there is a causal relationship between A and B. Linked seems to have become a code word for unknown causal relationship. Be skeptical and investigate the data and reasoning involved with such claims.

8. Chapter 10 Mill’s Methods for Causal Reasoning John Stuart Mill created two methods for finding causal relationships and one joint method. The Method of Agreement is a test for a necessary condition. Any factory that is absent when G is present is eliminated as a possible necessary condition of G. The Method of Difference is a test for a sufficient condition.  Any factor that is present when G is absent is eliminated as a possible sufficient condition of G. The Method of Agreement and Difference is a joint test for necessary and sufficient conditions. Review pages 296-298 for detailed explanations.

9. Chapter 10 Inference to the Best Explanation (IBE) The general form of an IBE is the following: • D exists. • H 1 would explain D. • H 1 would offer the best explanation of D. Therefore, probably, 4. H 1. Where D stands for data and H1 stands for a hypothesis. We call IBEsabductive arguments following the language of C.S. Peirce. Abductive arguments lead to an explanatory hypothesis according to Pierce.

10. Chapter 10 When evaluating IBE arguments, the third premise needs support via a subargument and we can detail it this way. • Plausibility • Empirically adequate • Probable • Not ‘ad hoc’ (for this purpose hypothesis” • Falsifiability – means makes a genuine assertion that is compatible with some data an incompatible with other data. Now what makes one explanation better than another.

11. Chapter 10 Here are some considerations for comparing explanations: • More plausible than the alternatives • Explains more/has more explanatory power • Simplicity – the explanation is more simple These condition in conjunction with plausibility and falsifiability are what give rise to cogent, inductive IBE arguments. But we should be careful with IBE arguments because of how we support the third premise.

12. Chapter 10 Errors and Fallacies in Causal Reasoning The Post Hoc Fallacy – this is sometimes called the post hoc ergo propter hoc fallacy. The full phrase means: “after this, therefore because of this” And it is a causal inference fallacy. (304) Just because one thing comes before another does not mean that the first thing was causally relevant to the second, and this is just what the post hoc fallacy claims. It is classic bad reasoning.

13. Chapter 10 The fallacy of objectionable cause: occurs when a person argues for a causal interpretation on the basis of limited evidence and makes not attempt to rule out alternative explanations of the event. (306) Sometimes this fallacy is called the false causefallacy. This kind of fallacy happens a lot in election debates and it is similar to a confusion of correlation with causation.

14. Chapter 10 Begging the Question in a Causal Account: This is just a case of begging the question in a situation where there is some causal claim our account in question. So, a person assumes the conclusion or something logical equivalent to the causal conclusion as support for it. See page 308 for a detailed example.

15. Chapter 10 Causal Slippery Slope Arguments: Causal Slippery Slope fallacy claims in the premises that some action would be wrong because it would let off a series of side effects ending ultimately in general calamity. Causal slippery slope fallacies can also go in the other direction claiming that something would be good because it would give rise to certain good effects. Fixing these fallacious arguments generally requires providing cogent subarguments for the premises.

16. Chapter 10 Terms for review: Abductive argument ad hoc hypothesis Causal inductive argument Causal slippery slope Correlation Explanatory Power Fallacy of objectionable cause Falsifiability Inference to the Best Explanation (IBE) Plausibility Post hoc fallacy Simplicity