Causal relationship, inus definition, and suppes Definition

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Causal relationship, inus definition, and suppes Definition

We know that causal relationships are very important for us to think about any problem and do anything. Many people even think that the causal association ability of talent is one of the first conditions for human beings to become a "Everlasting thing. In philosophy, there are indeed opinions that doubt or even contradict the causal relationship, and these opinions cannot even be refuted. However, these opinions can be played and cannot be taken seriously. The negative causal relationship can only lead to an unknown theory.

Without the causal relationship, it is almost impossible for us to acquire knowledge through experience. The curse of hraclitus is about to become a reality, and the real world will become a river that will never step into again. What we can gain is only one fragmented observation statement with no relationship between them. And these statements are about the past, which is not helpful for us to predict the future or take actions to cope with the future. Such a world is of course terrible.

We seem to be born with the ability to establish causal associations, and this impulse. For example, when we are stabbed by a thorn, we will avoid seeing other sharp hard things. For example, we liked to ask "why" from an early age, and we felt happy when we got an answer. But what is a causal relationship or how to identify whether it is a causal relationship is not clear to everyone.

Obfuscation or divergence in the understanding of causal relationships can severely impede consensus among people. The determination of the causal relationship sometimes affects the division of "responsibility", and sometimes affects decisions such as "What should we do now. If the two sides do not define the causal relationship in a uniform way, there will be no unnecessary debate.

Establishing a causal relationship is also an instinct of science. The causal relationship is very convincing. "Knowledge is power." This power comes largely from the concept of causation. Therefore, if you understand the causal relationship Hu tu, the results of scientific research are also very confused. Then it is very dangerous to put the conclusion of Hu tu into practice.

There are two most common mistakes in causal relationship. First, the cause and effect relationship is considered to have no cause and effect relationship when there are no sufficient or necessary conditions. The second is to mix the causal relationship with the statistical correlation, and find the positive correlation in the data, that is, find the causal relationship.

These two kinds of errors, the former usually correspond to a single empirical event or a relatively direct causal association, and the latter usually corresponds to a large number of empirical events or complicated causal associations. To avoid these two mistakes, we need to understand the two definitions of causal relationship: inus and suppes.

The following two examples are used to describe them separately.

In the first example, a Party A pushed down a Party B from the fifth floor, and B died after landing. Generally, Party A is responsible for this, because we think that the reason why Party A pushes Party B down the stairs is the death of Party B. However, if sufficient/necessary conditional relationships are used to test the cause and effect relationship, this causal relationship is not true. Because even if a person is not pushed down, he may not be dead. Therefore, it is not necessary for a person to push a person to the next floor. On the other hand, if a certain B has just landed on an inflatable mat, or a certain B has a peerless power, then he may not die even if he is pushed down, so there is no sufficient condition here.

This is obviously contrary to our common sense. It can be seen that the causal relationship is not necessarily a sufficient and necessary conditional relationship. Here we need the definition of inus, which can help us solve this problem.

The definition of inus is proposed by a person named marqi, so it is also called the definition of marqi ". Inus is the abbreviation of its definition:

An insufficient but necessary part of a condition which is itself unnecessary but sufficient for the result. (an incomplete but necessary component of an unnecessary but sufficient condition of the result)

This statement can be further reduced:

The reason is a necessary component of a sufficient condition group of results.

That is to say, assume that A and B are two events. In the following cases, we call a reason for B:

1. When a is added with some other events, a composite condition C can be formed, and C is a sufficient condition of B;
2. If a is removed from C, C is no longer a sufficient condition for B.

In the example above, it is not difficult to find that:

1. "A party pushes a Party B Down The Stairs" and "downstairs is hard", and "A Party B will not be superior to others" is a sufficient condition for "A Party B's death;
2. Simply adding "downstairs is hard" and "a certain Party B will not be able to beat down" is not a sufficient condition for "a certain Party B's death.

From this we can draw a conclusion that "a party pushes B Down The Stairs" is the cause of "B's death.

Of course, we can also see from the above that there is usually more than one reason for a single result. So what is the "main reason? People who have been fooled by Dialectics may ask this question. In fact, there is no "main reason" logically. In practice, we will choose to use them according to the context.

For example, "there is no inflatable mat downstairs" and "a B won't be superior to others" are also the cause of "a B's death. However, we do not want to say that Party B deserves it, or ask the host of the Land why not put an inflatable mat. This is because in the determination of responsibility, apart from the determination of causal relationship, there are also disclaimers such as "unanticipated" and "unselectable.

The inus definition can also help us understand why the cause is not necessarily the cause, and the result is not necessarily the result.

The above is a single experience event. For a large number of empirical events, we usually use statistical methods for analysis. Statistics can establish a data correlation. However, the statistical positive correlation, even a very high positive correlation, is not equal to the causal relationship.

In one example, it is said that some people have done statistics. The more lighters there are in the house, the higher the proportion of the male master who suffers from cough and asthma. Therefore, putting too many lighters at home is a cause of cancer. This conclusion is obviously absurd. The general explanation is that smoking people have more lighters, while smoking people suffer from cough and asthma. Therefore, a lighter is not the cause of cough and asthma, but the result of smoking is the same as that of cough and asthma.

However, this example does show that the positive correlation of statistics is not necessarily a causal relationship. Is there a way to verify whether positive correlation in statistics contains a causal relationship? Here we need the suppes definition.

Suppes is defined by a person called suppes. In this definition, the positive correlation in probability is called "initial reason ". The "initial cause" may be "actual cause" or "false cause ". The premise for becoming a real cause is that there is no such event. Given this event, this positive correlation tends to disappear.

In the above example, we can find that the number of lighters is obviously a false reason. This positive correlation tends to disappear if we only consider the person who smokes or the person who does not smoke.

Is "Smoking" a real reason? So far, we do not seem to have provided any incidents, which can eliminate the correlation between smoking and cough and asthma. However, the answer is still no. The so-called "real reasons" in the definition of suppes cannot be confirmed, because "there is no pseudo-Event" itself is an unverifiable premise.

For example, suppose there is a gene that determines whether a person will be addicted to smoking and whether the person will suffer from cough and asthma. Then, for those with or without such genes, the relationship between smoking and cough and asthma disappears.

Therefore, the suppes definition gives us a way to prove the "initial cause. If we want to verify whether a statistical correlation contains a causal relationship, we can find a condition that can make such a correlation disappear. If it is found, it is a false reason. Before finding it, we may assume that it is a real reason.

In addition to the inus definition and suppes definition, there are also other methods for defining the causal relationship. But they are indeed the best in terms of understanding and application. In my opinion, it is closely related to everyone's thinking and life. Its important value is not inferior to Newton's Law of mechanics, and its frequency of use is far greater than that. The popularization of such common sense should really start with dolls. Unfortunately, our middle school does not offer logic courses, and there is no scientific methodology course in the university. It's no wonder that the logical and chaotic youth meeting of literature is filled with heaven and earth, and the "scholar" group of the mind Hu tu also forms a "beautiful landscape" of this "ancient civilization ".