Tucked away in ACEs research, often toward the end of studies, is the all too common phrase that their studies are not able to discern "causal inference
", meaning we don't really know if ACEs caused the bad outcome in the study, or, if it does, we don't know by how much, or even how.
However, to quantify the causal inference
produced, statistical techniques are commonly used that contrast the association among the variables of interest, not precisely of causal effect.
What does "causal inference
" mean, and how can we use it to make decisions?
Inference on the relationship between cause and effect is called causal inference
. Some of the major methods of causal inference
are illustrated below (The Economics of Cause and Effect - Thought Process that Catches Truth from Data, Makiko Nakamuro and Yusuke Tsugawa, Diamond, Feb.
(5) Judea Pearl, "Causal Inference
in Statistics: An Overview," Statistics Surveys 3 (2009): 96-146, doi:10.1214/09-SS057; and Christopher Winship and Michael Sobel, "Causal Inference
in Sociological Studies," in Handbook of Data Analysis, ed.
In this example, gender is called a confounder in causal inference
literatures. Although the new instruction method increases the score of both boys and girls, the imbalance of the gender distribution in two schools may confound the effect of the new instruction method.
Nutritional epidemiology is one of the most challenging fields in epidemiology with respect to causal inference
. Eating or not eating a particular food often occurs within a much wider dietary habit pattern, thus making it difficult to statistically disentangle the effects of individual foods.
was performed to evaluate the causal relationship between a causal variable and the target variable.
Whatever Phase II and III clinical trials or CER, properly designed and carried out RCTs can provide the most definitive causal inference
. Compared with RCTs of Phase II and III, which can produce persuasive causal inference
, the challenge for CER should be considered cautiously because the cleanest comparison occurs only when standard interventions are performed, i.e., there are no unexpected co-interventions, such as medications, supplementary therapies, and behaviors during trials.
Advanced causal inference
methods are an appealing approach to meeting this need because such methods can reduce biases stemming from imbalances in observed characteristics (when assignment is nonrandom) in patients with and without medical homes.