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Contrasting predictive and causal values of predictors …

One kind of criticism has focused on the psychological implausibilityof Lewis's explanation. (See Horwich 1987.) Recall that theexplanation appeals, on the one hand, to a system of weighted respectsof similarity between possible worlds that is delivered by conceptual analysis and, on the other hand, to an asymmetryof overdetermination that is claimed to be a contingent truth about the actual world. The two-part explanationis supposed to employ facts that are sufficiently well known to play arole in the explanation of our linguistic use ofcounterfactuals. However, it is psychologically implausible that theintricate system of weighted respects of similarity involvingcomparison of miracles of different sizes could capture the intuitivesimilarity relation used in counterfactual reasoning. Why should wehave developed such a baroque notion of similarity? Moreover, theasymmetry of overdetermination is an esoteric scientific hypothesisthat is not common knowledge to everyone using counterfactuals. So itis very unlikely that this hypothesis could account for ordinaryspeakers' mastery of the temporal asymmetry of counterfactuals. (ForLewis's reply to this criticism see Postscript E to “CounterfactualDependence and Time's Arrow” in his (1986a, p. 66).)

This paper examines differences between predictive and causal learning in humans.

Systematic reviews are currently favored methods of evaluating research in order to reach conclusions regarding medical practice. The need for such reviews is necessitated by the fact that no research is perfect and experts are prone to bias. By combining many studies that fulfill specific criteria, one hopes that the strengths can be multiplied and thus reliable conclusions attained. Potential flaws in this process include the assumptions that underlie the research under examination. If the assumptions, or axioms, upon which the research studies are based, are untenable either scientifically or logically, then the results must be highly suspect regardless of the otherwise high quality of the studies or the systematic reviews. We outline recent criticisms of animal-based research, namely that animal models are failing to predict human responses. It is this failure that is purportedly being corrected systematic reviews. We then examine the assumption that animal models can predict human outcomes to perturbations such as disease or drugs, even under the best of circumstances. We examine the use of animal models in light of empirical evidence comparing human outcomes to those from animal models, complexity theory, and evolutionary biology. We conclude that even if legitimate criticisms of animal models were addressed, through standardization of protocols and systematic reviews, the animal model would still fail as a predictive modality for human response to drugs and disease. Therefore, systematic reviews and meta-analyses of animal-based research are poor tools for attempting to reach conclusions regarding human interventions.

Example Of Associative And Causal Hypothesis

On the other hand, the null hypothesis is straightforward -- what is the probability that our treated and untreated samples are from the same population (that the treatment or predictor has no effect)? There is only one set of statistical probabilities -- calculation of chance effects. Instead of directly testing H, we test H. If we can reject H, (and factors are under control), we can accept H. To put it another way, the fate of the research hypothesis depends upon what happens to H.

the opposite of the research hypothesis. The null hypothesis states that any effects observed after treatment (or associated with a predictor variable) are due to chance alone. Statistically, the question that is being answered is "If these samples came from the same population with regard to the outcome, how likely is the obtained result?"

Example of associative and causal hypothesis

When we pose a research question, we want to know whether the outcome is due to the treatment (independent variable) or due to chance (in which case our treatment is probably not effective). For example, the claim that tutoring improves math performance generally does not predict exactly how much improvement. Each level of improvement has a different probability associated with it, and it would take a long time and a great deal of effort to specify the probability of each of the possible outcomes that would support our research hypothesis.

Several studies have shown that predictive and causal judgments vary depending on whether the question used to assess the relationship between events is presented after each piece of information or only after all the available information has been observed.

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ing causal hypotheses about theoretical constructs

The (or hypotheses -- there may be more than one) is our working hypothesis -- our prediction, or what we expect to happen. It is also called the - because it is an alternative to the null hypothesis. Technically, the claim of the research hypothesis is that with respect to the outcome variable, our samples are from different populations (remember that refers to the group from which the sample is drawn). If we predict that math tutoring results in better performance, than we are predicting that after the treatment (tutoring), the treated sample truly is different from the untreated one (and therefore, from a different population).

Prediction vs Hypothesis - Mad About Science!

Because nonhuman animal models (hereafter referred to as animal models or animals) have on multiple occasions been unsuccessful in predicting human response to drugs and disease (we will address this claim in depth), many have called for SRs in order to improve the models [-]. An example of this predicament would be the animal models used to determine which drugs to develop in an attempt to diminish neurological damage from ischemia events of the central nervous system (CNS) [, -]. By analyzing animal-based research with SRs, flaws in the methodology would also become apparent thus leading to eventual standardization of such studies. This would ostensibly also lead to better predictive values for humans (see table for calculating such values). Bracken supports this, stating:

Cosmic censorship hypothesis - Wikipedia

Although a tendency toward overshadowing was found between predictors, reliable overshadowing was observed only between causes, and then only when the test question was causal.

Statistical Modeling, Causal Inference, and Social Science

While some scientists are more modest in their claims for the value of SRs and the standardization of protocols, clearly there are high hopes for what SRs can accomplish regarding the predictive value of animal models. We will now examine, in more depth, the reasons for the above concerns regarding the predictive value of animal models.

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