The Fundamentals of Political Science Research, 2nd Edition Chapter 3: Evaluating Causal Relationships

Chapter 3 Outline Causality and everyday language Four hurdles along the route to establishing causal relationships Why is studying causality so important? Three examples from political science

Bivariate theories and a multivariate world Recall that the goal of political science (and all science) is to create and then evaluate causal theories. Most causal theories -- almost all of them -- are bivariate, which means that they ask whether a single independent variable is a cause of a single dependent variable. In a generic way, we'll usually say that we're wondering whether X causes Y . But, crucially, even though our theory might be bivariate, the real world is not. The real world is a place that is multivariate. That is, every interesting phenomenon (or dependent variable) has several causes. So, in the real world, perhaps X does cause Y -- or perhaps not – but the key is to remember that perhaps some other variables, which we'll call Z, might cause Y , too (or instead).

Evaluating bivariate theories in a multivariate world So if our theories are bivariate, but reality is multivariate, we need to figure out how to control for the other possible causes of Y when evaluating whether X causes Y. If we don't control for Z, the other possible causes of Y , then our conclusions about whether X causes Y might very well be mistaken. Precise strategies, which we'll call research designs, for doing this will be the subject of Chapter 4. What we'll talk about in Chapter 3 are the logical foundations about how to evaluate causal connections.

A note on the word “causality” The words “cause” or “causality” appear everywhere. But what do we mean by it in Political Science? In fact, the branch of Philosophy of Science that is dedicated to eshing out what we mean by “causality” is quite extensive. In most understandings, “causality” is understood as deterministic. But social reality is more complex than physics, so in our world, causation is normally understood as probabilistic. What are the implications of this?

The focus on causality Recall that the goal of political science (and all science) is to evaluate causal theories. Bear in mind that establishing causal relationships between variables is not at all akin to hunting for DNA evidence like some episode from a television crime drama. Social reality does not lend itself to such simple, cut-and-dried answers. Is there a “best practice” for trying to establish whether X causes Y ?

The four causal hurdles 1. Is there a credible causal mechanism that connects X to Y ? 2. Can we rule out the possibility that Y could cause X? 3. Is there covariation between X and Y ? 4. Have we controlled for all confounding variables Z that might make the association between X and Y spurious?

Hurdle 1 What do we mean “Is there a credible causal mechanism that connects X to Y ?” Can you answer the “how” and “why” questions?

Hurdle 2 Can we rule out the possibility that Y could cause X? It's possible that X  Y , Y  X, or X ↔ Y , or neither.

Hurdle 3 Is there covariation between X and Y ? This is the easiest one. No, correlation is not causation, but it's normally a key component of causation.

Hurdle 4 Have we controlled for all confounding variables Z that might make the association between X and Y spurious? This is the toughest hurdle to cross in most social sciences.

But what if we don't cross that fourth hurdle? A substantial portion of disagreements between scholars boil down to this fourth causal hurdle. When one scholar is evaluating another's work, perhaps the most frequent objection is that the researcher “failed to control for” some potentially important cause of the dependent variable. So long as a credible case can be made that some uncontrolled-for Z might be related to both X and Y , we cannot conclude with full condence that X indeed causes Y. Since the main goal of science is to establish whether causal connections between variables exist, then failing to control for other causes of Y is a potentially serious problem. One of the themes of this class is that statistical analysis should not be disconnected from issues of research design -- like controlling for as many causes of the dependent variable as possible.

Applying the four hurdles Are we talking merely about political science research here? Absolutely not. Where else would these thinking skills apply?

The path to evaluating a causal relationship

The causal hurdles scorecard Can we get from [????] to [yyyy]?

Life satisfaction and democratic stability What is the relationship between life satisfaction in the mass public and the stability of democratic institutions? Inglehart (1988) argues that life satisfaction (X) causes democratic system stability (Y ). If people in a democratic nation are more satised with their lives, they will be less likely to want to overthrow their government. Stay focused on the causal hurdles. How can we evaluate this claim?

Race and political participation in the U.S. What is the relationship between an individual's race and the amount of political participation that individual engages in? Many scholars have noticed that Anglos participate more in politics than do African Americans. But is that relationship causal? What confounding (Z) variables would we need to control for that might shed light on this relationship?

Evaluating whether Head Start is effective Does attending Head Start affect Kindergarten readiness? What confounding (Z) variables would we need to control for that might shed light on this relationship?