 Welcome to this web lecture on empirical legal research. Today, we're going to discuss the topic of causality. Causality is an important topic because we're often interested in whether the reform or the new law or the new rule has an effect on behavior, decisions, or other outcomes. Three criteria have to be met in order to conclude or prove causality. The first one is that there needs to be a relationship, or as we call it, a correlation. This is an obvious requirement because if there's no relationship, we cannot conclude that the relationship is causal. A little bit more tricky is the temporal order. The temporal order implies that there needs to be a clear cause and a clear effect. I'll illustrate this with an example. The example is on human rights research. So let's suppose that a researcher is interested in the question whether the ratification of human rights treaties have an effect on human rights compliance. In other words, if a country signs a treaty on human rights, is it going to be more compliant with those human rights? Here, there's a clear temporal order because the researcher expects that the ratification has an effect on compliance. But is this really the good temporal order? Is it the proper order? It might be that it's exactly the other way around. It might be that human rights compliance actually has an effect on ratification. In this scenario, the country would first comply with those human rights and then think that it would be a wise idea to ratify human rights treaties. So if the temporal order is not clear, like in this example, in other words, if we do not know what is the cause and what is the effect, whether the ratification of human rights treaties has an effect on human rights compliance or whether human rights compliance has an effect on the ratification of human rights treaties, then there's no clear temporal order. And if there's no clear temporal order, we cannot prove causality. Then we go to the third criterion, spuriousness. And there's two ways that a relationship can be spurious. One is that the relationship is based on coincidence. The other one is that there's rival explanations. Well, let me start with the first example. Relationship based on coincidence. And this is a ridiculous example. So here we see that there's a very strong relationship between the movies, the number of movies that Nicholas Cage appears in, and the number of people who drown by falling into a pool. No serious human being would conclude that there's a causal relationship here. You see that there's a very strong relationship, but the relationship is based on coincidence. A similar example is the relationship between cheese consumptions on the one hand and the number of people who die by becoming tangled in their bedsheets. There's absolutely no causal relationship here. One cannot conclude that if the cheese consumption goes down, goes down, that the number of people who die in their bedsheets also goes down. Similarly, the age of Miss America is very highly correlated with the murders by steam, hot vapors, and hopped objects. Also here one would not conclude that there's a causal relationship between the age of Miss America and the number of murders. It's not that if the age of Miss America goes down, the number of murders by steam, hot vapors, and hopped objects also go down. So here what we see in all these free examples is we observed a similar pattern. We see that there's a very strong relationship, but from this relationship we cannot conclude that it's causal because it's spurious, it's based on coincidence. So now a more serious example, because we all understand that these examples are no causal relationships. A little more difficult example is the example of medical malpractice. And this shows how rival explanations can disturb our original idea. Medical malpractice is easy to understand. The doctor makes a mistake and the patient gets harmed. But now suppose that a researcher expects or is interested in the question whether the number of claims have an effect on what physicians do. So do physicians order new tests, or are they going to stop seeing risky patients if the number of claims go up? We assume here that there's a causal relationship that the number of claims affects physicians' behavior. But is that really the case? There could be some rival explanations here. I'll give you two examples of possible rival explanations. One is technology. So suppose that there's new technologies available to doctors that they can perform new tests, order new tests, or diagnose patients differently. That explains why physicians change their behavior. And it could also be that these same technologies determine the number of claims. Because if there's new technologies available, it becomes easier to determine whether a doctor made a mistake and also to file a claim. Because when it's easier to see whether the doctor made a mistake, the number of claims might go up. If this is the case, we see that technology or technological advancements, both explain physician behavior and also the number of claims. This is a clear example of a spurious relationship. Not that the number of claims does not have an effect on physicians' behavior. It's the third variable technology that explains both events. Similarly, if there's more societal pressure, pressure from the public, then doctors might be more trying to be more safe and to provide more safe diagnoses by ordering different tests or do more testing. If that is the case, the societal pressure also influences physician behavior, just like technology. And it could also explain when a number of claims go up. Because if people start to complain earlier and more frequently, then they'll probably start to claim frequently, more frequently as well. So here we also could assume, or at least argue, that the societal pressure explains both the physician behavior as well as the number of claims. So here we see the three criteria. One is that there needs to be a relationship between two variables. That's the easy criterion. There needs to be a clear temporal order. There needs to be a clear cause and a clear effect. And the relationship between the two variables cannot be spuriousness. It cannot be based on coincidence and we have to rule out rival explanations. If one of these criteria cannot be met, we cannot conclude that there's a causal relationship. And if we want to conclude that there's a causal relationship, we need to prove it and include it in our research design. Thank you for watching this lecture. I hope to see you next time.