Statistical Models for Causation
David A. Freedman · 2007
We review the basis for inferring causation by statistical modeling.Parameters should be stable under interventions, and so should error distributions.There are also statistical conditions on the errors.Stability is difficult to establish a priori, and the statistical conditions are equally problematic.Therefore, causal relationships are seldom to be inferred from a data set by running statistical algorithms, unless there is substantial prior knowledge about the mechanisms that generated the data.We begin with linear models (regression analysis) and then turn to graphical models, which may in principle be non-linear.