Causal Concepts, Principles, and Algorithms

It is an important truism that association is not causation. For example, people living in low-income areas may have higher levels of exposure to an environmental hazard and also higher levels of some adverse health effect than people living in wealthier areas. Yet this observed association, no matter how strong, consistent, statistically significant, biologically plausible, and well documented by multiple independent teams, does not necessarily tell a policy maker anything about whether or by how much a proposed costly reduction in exposure would reduce adverse health effects. Perhaps only increasing income, or something that income can buy, would reduce adverse health effects. Or maybe factors that cannot be changed by policy interventions increase both the probability of living in low-income areas and the probability of adverse health effects. Whatever the truth is about opportunities to improve health by changing policy variables, it typically cannot be determined by studying correlations, regression coefficients, relative risks, or other measures of association between exposures and health effects (Pearl 2009). Observed associations between variables can contain both causal and non-causal (“spurious”) components. In general, the effects of policy changes on outcomes of interest can only be predicted and evaluated correctly by modeling the network of causal relationships by which effects of exogenous changes propagate among variables. The chapter reviews current causal concepts, principles, and algorithms for carrying out such causal modeling and compares them to other approaches.