63 research outputs found
Estimating Latent Causal Influences: TETRAD III Variable Selection and Bayesian Parameter Estimation
No full textThe statistical evidence for the detrimental effect of exposure to low levels of lead on the cognitive capacities of children has been debated for several decades. In this paper I describe how two techniques from artificial intelligence and statistics help make the statistical evidence for the accepted epidemiological conclusion seem decisive. The first is a variable-selection routine in TETRAD III for finding causes, and the second a Bayesian estimation of the parameter reflecting the causal influence of Actual Lead Exposure, a latent variable, on the measured IQ score of middle class suburban children
Unidimensional Linear Latent Variable Models
No full textRichard Scheines. Unidimensional Linear Latent Variable Models
The Similarity of Causal Inference in Experimental and Non-Experimental Studies
No full textFor nearly as long as the word “correlation” has been part of statistical parlance, students have been warned that correlation does not prove causation, and that only experimental studies, e.g., randomized clinical trials, can establish the existence of a causal relationship. Over the last few decades, somewhat of a consensus has emerged between statisticians, computer scientists, and philosophers on how to represent causal claims and connect them to probabilistic relations. One strand of this work studies the conditions under which evidence accumulated from non-experimental (observational) studies can be used to infer a causal relationship. In this paper, I compare the typical conditions required to infer that one variable is a direct cause of another in observational and experimental studies. I argue that they are essentially the same
Unidimensional linear latent variable models
No full textAbstract: "Linear structural equation models with latent (unmeasured) variables are used widely in sociology, psychometrics, and political science. When such models have a unidimensional (pure) measurement model (Gerbing and Anderson 82, 88; Scheines 92) they imply constraints on the measured covariances which can be used to either confirm unidimensionality or find submodels which are unidimensional. Assuming unidimensionality, the causal relations among the latent variables can be partially determined by examining other (related) constraints on the measured covariances.In this paper I prove first that unidimensionality is detectible from constraints on only the measured covariances no matter what the structure among latent variables, and second that in a structural equation model with a unidimensional measurement model, for any three latents T[subscript i], T[subscript j], and T[subscript k], [rho]T[subscript i],T[subscript j].T[subscript k] = 0 only if certain constraints hold on only the measured covariances.
Computer environments for proof construction
No full textAbstract: "Does the presentation of search space matter for complex problem solving tasks? We address this question for the construction ofproofs in sentential logic by comparing three computerized environments and measuring their relative pedagogical effectiveness. After beinggiven a pretest for logical aptitude, three matched groups were presented identical course material on logic for approximately five weeks. Problems from this material were completed in three different computerized environments, however. During that time, all students completedover one hundred exercises and then took a midterm. The group using the most informative and flexible interface performed substantially better on the midterm, with nearly all the difference arising in the harder problems.In two follow up experiments we added strategic problem solving help; student performance improved again (entirely on the more difficult problems).
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