Foster (2010). Causal inference and developmental psychology.” – Article summary

The problem of causality is difficult in developmental psychology, as many questions of that field regard factors that a person cannot be randomly assigned to (e.g. single parent family). Causal inference refers to the study and measurement of cause-and-effect relationships outside of random assignment.

In the current situation in developmental psychology, it is unclear among researchers whether causality can be implied and why. Causal inferences are necessary for the goals of developmental psychology because causal inferences can improve the lives of people (1), can help distinguish between associations and causal claims for laypeople (2) and causal thinking is unavoidable (3).

The directed acyclic graph (DAG) is a tool which is useful in moving from associations to causal relationships. It is particularly useful in identifying covariates and understanding the anticipated consequences of incorporating these variables.

The DAG is a symbolic representation of dependencies among variables. The causal Markov assumption states that the absence of a path (in the DAG) implies the absence of a relationship. In the DAG, models that represent data with fewer links are preferred to the more complex (parsimony). If two variables are simultaneously determined, the DAG could incorporate this possibility by treating the two as reflecting a common cause.

Variables (in the DAG) can be related in three ways:

1. Z is a common cause of X and Y
   In this case, Z needs to be controlled for.
2. Z is a common effect of X and Y
   This is a collider. Conditioning on a collider creates a spurious relationship between X and Y. This relationship can suppress or inflate a true causal effect.
3. Z mediates the effect of X on Y