The term multifinality refers to a condition in which the same cause leads to different outcomes. Although the concept of multifinality may seem trivial at first glance, it has posed serious challenges to the concept of causality that happens to lie at the heart of science. In fact, the concept of causality is so important that scientists work tirelessly throughout their lives to identify the causes of important outcomes (conflict, aggression, anger, etc.). Of course, scientists are not unique in their preoccupation with causal relationships. Indeed, most people work tirelessly throughout their lives trying to figure out the factors that cause a range of important outcomes, from the desired affection of a potential love interest to success in the boardroom. From a practical standpoint, the ability to understand causal relationships carries enormous benefits because knowing the factors that cause important outcomes provides the key to predicting and controlling those outcomes. For example, the knowledge that X (parenting skills) causes the outcome Y (childhood achievement) can be used to manipulate X (through training in parenting skills) to cause changes in the outcome of Y (increased childhood achievement).
Although the exact criteria for assuming a causal relationship between two variables has long been a subject of debate, most scholars agree that at least three important criteria must be met to assume that one variable causes another. First, the two variables must covary such that changes in the first variable correlate with changes in the second variable. Second, the variable assumed to be the cause (e.g., poor parenting skills) must precede in time the observation of the outcome or effect variable (e.g., low childhood achievement). Third, all alternative explanations (e.g., low socioeconomic status, unstable home environment, genetic factors, etc.) must be ruled out before concluding that the proposed causal variable X (poor parenting skills) did cause the outcome Y (low childhood achievement). Assuming these criteria are satisfied, one can tentatively assume that X causes Y.
Although the concept of causality may appear straightforward, closer inspection reveals that what may seem to be a straightforward causal relationship can actually be quite complex in real life. Particularly in the social and behavioral sciences, multifinality and the related concept of equifinality qualify the ability to assume a direct causal relationship between a single cause (X) and a single effect (Y): Multifinality recognizes that sometimes the same cause (X) produces many different outcomes (Y1, Yv Y3), whereas equifinality recognizes that, at other times, different causes (X1, X2, X3) produce the same outcome (Y).
More specifically, equifinality recognizes that different causes may, nevertheless, arrive at a common outcome. Stated otherwise, many roads lead to the same end. For example, in developmental psychology, research suggests that the different developmental experiences (poor parenting skills versus low socioeconomic status) can lead to the same outcome (low childhood achievement). As noted earlier, multifinality is unique in its recognition that similar, or even seemingly identical, causes may lead to different outcomes. In developmental psychology, research suggests that sometimes the same developmental experiences (child abuse) can lead to different outcomes (high childhood achievement versus low childhood achievement). In addition, goal psychologists have shown that the same behavior (working out at the gym) can result in the satisfaction of many different goals (goal 1: improve fitness; goal 2: meet new people). As these examples illustrate, the concept of multifinality describes a case in which a single road can lead to many different destinations.
So, what does the concept of multifinality mean for the goal of identifying causal relationships between variables? Multifinality (as well as the sister concept of equifi-nality) poses a serious challenge to science because it reduces the ability to confidently conclude that one causal variable (X) always leads to a second outcome variable (Y). That is, multifinality ultimately makes it difficult to confidently conclude that a particular outcome (Y) is, and always is, the result of a particular causal condition (X). Although adding complication, the awareness of multifinality has enriched rather than invalidated the scientific pursuit of identifying causal relationships. Scientists have simply had to adjust the discourse and terminology they use when addressing the concept of causality to account for multifinality. Specifically, multifinality (and the sister concept of equifinality) has forced scientists to abandon attempts to discuss causal relationships between variables as statements of fact. Instead, scientists have refined their claims to present and discuss proposed causal relationships in the language of probability rather than fact. That is, scientists realistically incorporate a measure of error into their causal statements to account for multifinality, and to suggest that X is likely to cause rather than always causes Y, and that Yis likely rather than always caused by X. In this sense, the concept of multifi-nality has only prompted scientists to face the cold hard fact that, however probable, there is no such thing, including a causal relationship, that is absolutely certain in life.