Being Reduced: New Essays on Reduction, Explanation, and Causation

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Group Agency and Supervenience. Philip Pettit.

Distinctions in Distinction. Daniel Stoljar. Mental Causation and Neural Mechanisms. James Woodward. Dualism and Exclusion. Bram Vaassen - - Erkenntnis Laukyte Migle - - Ethics and Information Technology 19 1 Interventionism and the Exclusion Problem. Yasmin Bassi - - Dissertation, University of Warwick. Realization and Causal Powers. Umut Baysan - - Dissertation, University of Glasgow. Oxford University Press. Eronen - - British Journal for the Philosophy of Science 63 1 Gene Witmer - - Mind Added to PP index Total views 71, of 2,, Recent downloads 6 months 1 , of 2,, How can I increase my downloads?

Sign in to use this feature. The Exclusion Problem in Philosophy of Mind categorize this paper. Modern machines, he contends against a chorus of enthusiasts, are nothing like our minds. To make the stakes clear, consider the following scenario. Suppose there is a robust, statistically significant, and long-term correlation between the color of cars and the annual rate at which they are involved in accidents.


To be concrete, assume that red cars, in particular, are involved in accidents year after year at a higher rate than cars of any other color. When you go to buy a new car, should you avoid the color red in your quest to remain safe on the road? On the one hand, it could be that the human visual system is not as good at gauging the distance and speed of red objects as it is with other colors. In that case, red cars could be involved in more accidents because other drivers tend to misjudge the speed and distance of approaching red cars and so collide with them more often.

On the other hand, the correlation may have nothing at all to do with the dangerousness of the color itself. It could, for example, be the byproduct of a common cause. People who choose red cars may tend to be more adventurous and thrill-seeking than the average driver, and so be involved in proportionally more accidents. Then again, the correlation may have nothing to do with driving abilities at all. People who buy red cars may just enjoy driving more than other people, and spend more hours a year on the road.

In that case, one would expect there to be more accidents involving red cars even if the drivers are, on average, more careful and cautious than other drivers. All of these hypotheses would account for the observed correlation between the color of the car and the rate of accidents.

And one can easily think up other hypotheses as well. To make matters worse, the observed correlation could be the product of all of these factors working conjointly. In the other cases, the redness itself plays no causal role, but is merely an indicator of something else.

This toy example illustrates the fundamental problem of causal reasoning: How can we find our way through such a thicket of alternative explanations to the causal truth of the matter?

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How can we find our way through a thicket of alternative explanations to the causal truth of the matter? In some cases, the best advice is to look for more correlations, correlations between different variables. To test whether the higher rate of accidents is due to more time on the road, for example, we ought to control for time.

If the true cause of the original correlation lies in how much different drivers like to drive rather than in the color itself, then the correlation should vanish when we look at the association between car color and accidents-per-mile-driven or accidents-per-hour-driven, rather than accidents-per-year. This line of thought suggests that the trick to deducing the causal from the correlational is just to comb through a large enough data set for other correlations. According to this operationalist way of thinking, all the answers lie, somehow, in the data.

One just has to figure out how to pan through them appropriately to reveal the hidden causal gold nuggets. Pearl began his work on artificial intelligence in the s with this mindset, imparted to him by his education. For much of the scientific community throughout the twentieth century, the very idea of causation was considered suspect unless and until it could be translated into the language of pure statistics. The outstanding question was how the translation could be carried out. But step by painful step Pearl discovered that this standard approach was unworkable. Causation really cannot be reduced to correlation, even in large data sets, Pearl came to see.

In short, you will never get causal information out without beginning by putting causal hypotheses in. This book is the story of how Pearl came to this realization. The book should be comprehensible to any reader with sufficient interest to pause over some formulas to digest their conceptual meaning though the precise details will require some effort even by those with background in probability theory.

Consider two basic building blocks of such graphs. The distinction between these two structures has important consequences for causal reasoning. While controlling for a common cause can eliminate misleading correlations, for example, controlling for a collider can create them. Here are some simple examples. We know there is a positive correlation between a car being red and it being involved in an accident in a given year. So we start by thinking up various causal hypotheses and representing them by directed graphs: nodes connected by arrows.

On another hypothesis, some personality trait is the cause of both buying red cars and driving more, and driving more is the cause of more accidents per year. If the correlation still persists when either of these is controlled for, then we know that this causal structure is wrong. But how do we decide which causal models to test in the first place? For Pearl, they are provided by the theorist on the basis of background information, plausible conjectures, or even blind guesses, rather than being derived from the data.

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The method of causal graphs allows us to test the hypotheses, both by themselves and against each other, by appeal to the data; it does not tell us which hypotheses to test. This contrasts with the traditional statistical approach. Sometimes we find that none of the data we have at hand can decide between a pair of competing causal hypotheses, but new data we could acquire would allow us to do so.

And sometimes we find that no data at all can serve to distinguish the hypotheses.

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Illusions of causality: how they bias our everyday thinking and how they could be reduced

Some particularly sensitive souls find this epistemic gap—between theory and data—intolerable. It is an old and familiar story. But the data against which such a theory is evaluated must be observable: that is what makes them data, after all—they are what is given to us by experience. Hence a gap opens up between what we believe in the theory and the grounds we have to believe it the data.

Edited by Robert E. Goodin

This back-to-the-data approach has been tried many times—think behaviorism in psychology and positivism in physics—and has failed just as many. In statistics, the form it took, according to Pearl, was a renunciation of all talk of causation—since, as the Enlightenment philosopher David Hume pointed out in the seventeenth century, a causal connection between events is not itself immediately observable.

As Hume put it, we can observe conjunction of events—that one sort of event consistently follows another, for example—but not causal connexion. But what we commonly care about is effective interventions in the world, and which interventions will be effective depends on the causal structure. If the redness of a car is a cause of its being involved in accidents because red is harder to accurately see, then one will be safer buying a car of a different color.

If the correlation is merely due to a common cause, such as the psychology of the buyer, then you might as well go with the color you prefer. Avoiding the red car will not magically make you a better driver.

Episode 4.4 Understanding Causal Reasoning

Unsurprisingly, trying to suppress talk of causation—by contenting ourselves with discussion of correlations—left the field of statistical analysis in a mess. Indeed there is one universally recognized situation in which observed correlation is accepted as proof of causation: the randomized controlled trial RCT. Suppose we take a large pool of car buyers and randomly sort them into two groups, the experimental group and the control group.

Being Reduced: New Essays on Reduction, Explanation, and Causation

We then force the experimental group to drive red cars and forbid the control group from doing so. Nonreductive Physicalism. Physical Properties.

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