path analysis requires scientific thinking, as does every exercise in causal inference. Statistics, as frequently practiced, discourages it, and encouraged “canned” procedures instead.
Despite Pearl’s early work on Bayesian networks, he doesn’t seem to be very familiar with Bayesian statistics—the above comment really only applies to frequentist statistics. Model construction and criticism (“scientific thinking”) is an important part of Bayesian statistics. Causal thinking is common in Bayesian statistics, because causal intuition provides the most effective guide for Bayesian model building.
I’ve worked implementing Bayesian models of consumer behavior for marketing research, and these are grounded in microeconomic theory, models of consumer decision making processes, common patterns of deviation from strictly rational choice, etc.
That quote is from a section on history, with the context implying that “as frequently practiced” is likely to refer to an average over the 20th century, not a description of 2018.
Despite Pearl’s early work on Bayesian networks, he doesn’t seem to be very familiar with Bayesian statistics—the above comment really only applies to frequentist statistics. Model construction and criticism (“scientific thinking”) is an important part of Bayesian statistics. Causal thinking is common in Bayesian statistics, because causal intuition provides the most effective guide for Bayesian model building.
I’ve worked implementing Bayesian models of consumer behavior for marketing research, and these are grounded in microeconomic theory, models of consumer decision making processes, common patterns of deviation from strictly rational choice, etc.
That quote is from a section on history, with the context implying that “as frequently practiced” is likely to refer to an average over the 20th century, not a description of 2018.