English, Article edition: MLBOOLEAN: Stata module to implement Boolean logit and probit Bear F. Braumoeller

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Bookmark: http://trove.nla.gov.au/version/175300
Physical Description
  • code
Language
  • English

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Title
  • MLBOOLEAN: Stata module to implement Boolean logit and probit
Author
  • Bear F. Braumoeller
Physical Description
  • code
Notes
  • Boolean logit and probit are a family of partial-observability n-variate models designed to permit researchers to model causal complexity, or multiple causal "paths" to a given outcome. The various "paths" are modeled as latent dependent variables that are multiplied together in a manner determined by the logic of their (Boolean) interaction. If, for example, we wanted to model a situation in which diet OR smoking causes heart failure, we would use one set of independent variables (caloric intake, fat intake, etc.) to predict the latent probability of diet-related coronary failure (y1*), use another set of variables (cigarettes smoked per day, exposure to second-hand smoke, etc.) to predict the latent probability of smoking-related coronary failure (y2*), and model the observed outcome (y, or coronary failure) as a function of the Boolean interaction of the two: Pr(y=​1) =​ 1-([1-y1*] x [1-y2*]). Any combination of ANDs and ORs can be posited, and the interaction of up to five latent variables can be modeled. See Bear F. Braumoeller (2003), "Causal Complexity and the Study of Politics," Political Analysis 11(3): 209-233, for further details. This routine was previously available from SSC as boolean, which has now been removed.
  • Boolean, logit, probit, complexity, causation, multiple causal paths
  • RePEc:boc:bocode:s434001
Language
  • English
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