by Causality and the need to reform the teaching of statistics I will argue that realistic and thus scientifically relevant statistical theory is best viewed as a subdomain of causality theory, not a separate entity or an extension of probability. In particular, the application of statistics (and indeed most technology) must deal with causation if it is to represent adequately the underlying reality of how we came to observe what was seen … The network we deploy for analysis incorporates whatever time-order and independence assumptions we use for interpreting observed associations, whether those assumptions are derived from background (contextual) or design information … Statistics should integrate causal networks into its basic teachings and indeed into
Topics:
Lars Pålsson Syll considers the following as important: Statistics & Econometrics
This could be interesting, too:
Lars Pålsson Syll writes The importance of ‘causal spread’
Lars Pålsson Syll writes Applied econometrics — a messy business
Lars Pålsson Syll writes Feynman’s trick (student stuff)
Lars Pålsson Syll writes Difference in Differences (student stuff)
Causality and the need to reform the teaching of statistics
I will argue that realistic and thus scientifically relevant statistical theory is best viewed as a subdomain of causality theory, not a separate entity or an extension of probability. In particular, the application of statistics (and indeed most technology) must deal with causation if it is to represent adequately the underlying reality of how we came to observe what was seen … The network we deploy for analysis incorporates whatever time-order and independence assumptions we use for interpreting observed associations, whether those assumptions are derived from background (contextual) or design information … Statistics should integrate causal networks into its basic teachings and indeed into its entire theory, starting with the probability and bias models that are used to build up statistical methods and interpret their outputs. Every real data analysis has a causal component comprising the causal network assumed to have created the data set …
Thus, because statistical analyses need a causal skeleton to connect to the world, causality is not extra-statistical but instead is a logical antecedent of real-world inferences. Claims of random or “ignorable” or “unbiased” sampling or allocation are justified by causal actions to block (“control”) unwanted causal effects on the sample patterns. Without such actions of causal blocking, independence can only be treated as a subjective exchangeability assumption whose justification requires detailed contextual information about absence of factors capable of causally influencing both selection (including selection for treatment) and outcomes …
Given the absence of elaborated causality discussions in statistics textbooks and coursework, we should not be surprised at the widespread misuse and misinterpretation of statistical methods and results. This is why incorporation of causality into introductory statistics is needed as urgently as other far more modest yet equally resisted reforms involving shifts in labels and interpretations for p-values and interval estimates.