Читать книгу The Wiley Blackwell Companion to Medical Sociology - Группа авторов - Страница 50

Despite the value of answers to descriptive research questions, we often want to talk about the causes of the statistical associations we observe. In the physical sciences, causal research questions are addressed via experiments. As noted above, experiments are also used in medical sociology research, but more frequently we rely on observational data. Because observational data lack the statistical properties of experimental data, conclusions regarding causal processes are often tentative. However, over the past 20 to 30 years, methodologists have developed a framework for causal analysis with observational data referred to as the counterfactual framework (Morgan and Winship 2015; Pearl 2009).

The counterfactual framework has two components, the potential outcomes model and causal graphs. The potential outcomes model provides a precise statement of what we mean when we say that a focal independent variable has a causal effect on an outcome (Rubin 1974). In particular, we mean the difference in the outcome that would be observed if a given case experienced an alternative exposure of a focal independent variable than the exposure that was observed. Let us consider smoking during pregnancy as our focal independent variable and the child’s birthweight as our outcome. Then the causal effect of smoking would be defined as the difference in a child’s birthweight for a mother who smoked during pregnancy had she not smoked. It’s impossible, however, to observe both of these states simultaneously (i.e. the birthweight of a child from a mother who smoked, and the birthweight of the same child had the same mother not smoked). Instead, we do the best we can to construct a comparison group to estimate what birthweight would have been observed had the mother not smoked.

Figure 3.1 Causal graph of the relationships between adult children‘s education(ACE), a vector of mediators (M), and mortality(MOR). X represents a vector of pretreatment confounders (e.g., respondent education), and U1 and U2 represent potential unobserved pretreatment and posttreatment confounders, respectively.

The second component, causal graphs, provide a systematic approach to determining what strategies for causal analysis are available with a given set of data and which variables need to be included in the analysis (Pearl 2009). Causal graphs depict relevant variables for an analysis as nodes and causal relationships between the variables using directed edges from the predictor to the outcome. In addition, bidirected edges can be used to indicate that two variables share a common cause. The relationships indicated in a causal graph are non-parametric and include all possible interactions. Figure 3.1 illustrates an example of a causal graph of mortality. The graph indicates key confounders of the effect of education on mortality (X and U₁) as well as confounders of the effect of mediators on mortality (U₂). Confounders are variables that affect both the focal independent variable and the outcome. If confounders are not addressed, estimates of the causal effect of the focal independent variable on an outcome will be biased. In contrast, mediators are variables that are thought to transmit the effect of a focal independent variable to an outcome. A mediation analysis seeks to identify the mechanisms that underlie a causal process. In this causal graph, we see that some confounders are observed (e.g. education) while others are unobserved (e.g. non-cognitive resources). The relationships depicted in this graph and the presence of observed and unobserved confounders have implications for strategies for estimating causal effects.