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Class Notes in Statistics and Econometrics Part 9

CHAPTER 17 Causality and Inference. This chapter establishes the connection between critical realism and Holland and Rubin’s modelling of causality in statistics as explained in [Hol86] and [WM83, pp. 3–25] (and the related paper [LN81] which comes from a Bayesian point of view). | CHAPTER 17 Causality and Inference This chapter establishes the connection between critical realism and Holland and Rubin s modelling of causality in statistics as explained in Hol86 and WM83 pp. 3-25 and the related paper LN81 which comes from a Bayesian point of view . A different approach to causality and inference Roy97 is discussed in chapter section 2.8. Regarding critical realism and econometrics also Dow99 should be mentioned this is written by a Post Keynesian econometrician working in an explicitly realist framework. Everyone knows that correlation does not mean causality. Nevertheless experience shows that statisticians can on occasion make valid inferences about causality. It is therefore legitimate to ask how and under which conditions can causal 473 474 17. CAUSALITY AND INFERENCE conclusions be drawn from a statistical experiment or a statistical investigation of nonexperimental data Holland starts his discussion with a description of the logic of association a flat empirical realism as opposed to causality depth realism . His model for the logic of association is essentially the conventional mathematical model of probability by a set U of all possible outcomes which we described and criticized on p. 12 above. After this Rubin describes his own model developed together with Holland . Rubin introduces counterfactual or as Bhaskar would say transfactual elements since he is not only talking about the value a variable takes for a given individual but also the value this variable would have taken for the same individual if the causing variables which Rubin also calls treatments had been different. For simplicity Holland assumes here that the treatment variable has only two levels either the individual receives the treatment or he she does not in which case he she belongs to the control group . The correlational view would simply measure the average response of those individuals who receive the treatment and of those who don t. Rubin recognizes in his .