On the Number of Experiments Sufficient and in the Worst Case Necessary to Identify All Causal Relations Among N Variables

We show that if any number of variables are allowed to be simultaneously and independently randomized in any one experiment, log2(N) + 1 experiments are sufficient and in the worst case necessary to determine the causal relations among N >= 2 variables when no latent variables, no sample selection bias and no feedback cycles are present. For all K, 0 < K < 1/(2N) we provide an upper bound on the number experiments required to determine causal structure when each experiment simultaneously randomizes K variables. For large N, these bounds are significantly lower than the N - 1 bound required when each experiment randomizes at most one variable. For kmax < N/2, we show that (N/kmax-1)+N/(2kmax)log2(kmax) experiments aresufficient and in the worst case necessary. We over a conjecture as to the minimal number of experiments that are in the worst case sufficient to identify all causal relations among N observed variables that are a subset of the vertices of a DAG.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI2005

Combining experiments to discover linear cyclic models with latent variables

Volume: Vol 9 : AISTATS 2010 Host publication title: Proceedings of the 13th International Conference on Artificial Intelligence and StatisticsPeer reviewe

Juego y deporte: deslindes, matices y mezcolanzas

El tema de la charla de hoy es la diferencia entre juego y deporte. Hay muchos autores que diferencian entre el juego y el deporte como diciendo que el deporte es un juego más reglado, más precisamente reglado con una complejidad más grande de reglas con respecto al juego. No creo, no estoy de acuerdo con esto, ni creo que esa sea la diferencia fundamental entre ambos. Creo que no hay juegos sin reglas, que la regla es tan privativa del deporte como del juego. La diferencia estaría en lo siguiente: el deporte es una manifestación lúdica tardía en el ser humano.Facultad de Humanidades y Ciencias de la Educació

When Are Multidimensional Data Unidimensional Enough for Structural Equation Modeling?:An Evaluation of the DETECT Multidimensionality Index

In structural equation modeling (SEM), researchers need to evaluate whether item response data, which are often multidimensional, can be modeled with a unidimensional measurement model without seriously biasing the parameter estimates. This issue is commonly addressed through testing the fit of a unidimensional model specification, a strategy previously determined to be problematic. As an alternative to the use of fit indexes, we considered the utility of a statistical tool that was expressly designed to assess the degree of departure from unidimensionality in a data set. Specifically, we evaluated the ability of the DETECT “essential unidimensionality” index to predict the bias in parameter estimates that results from misspecifying a unidimensional model when the data are multidimensional. We generated multidimensional data from bifactor structures that varied in general factor strength, number of group factors, and items per group factor; a unidimensional measurement model was then fit and parameter bias recorded. Although DETECT index values were generally predictive of parameter bias, in many cases, the degree of bias was small even though DETECT indicated significant multidimensionality. Thus we do not recommend the stand-alone use of DETECT benchmark values to either accept or reject a unidimensional measurement model. However, when DETECT was used in combination with additional indexes of general factor strength and group factor structure, parameter bias was highly predictable. Recommendations for judging the severity of potential model misspecifications in practice are provided.<br/