Causal research
A causal model is an abstract quantitative representation of real-world dynamics. Hence, a causal model attempts to describe the causal and other relationships, among a set of variables. Essentially, causal models are based onstructural equations of the form z = b1x + b2y, and are analysed using regression techniques. However, a simpler way to understand the principle of causal models is to think of them as hypotheses about the presence, sign, and direction of influence for the relations of all pairs of variables in a set. Usually these relations are mapped in diagrams or flow graphs as in the simple example shown below. Even when there are only three variables under examination many different models of their relationship are possible. Thus, investigating all the different possible models is an important step in the analysis of data, and in linking clinical theory to empirical research. We are among a small group of nonacademicians who are applying causal theory to understand the effects of drug therapy given an intermediate laboratory result or severity of illness measure that must be accounted for or would otherwise confound the analysis and result in spurious results. Our published papers explain the proposed causal pathways, selection of causal models, and assumptions made and tested for each particular application.
Thamer et al. Relationship between Prednisone, Lupus Activity and Permanent Organ Damage
Zhang et al. Epoetin and survival among elderly hemodialysis patients