Sentence examples for value of the causal from inspiring English sources

We get from there to a causal explanation if we can intervene so as to manipulate the value of the causal variable by itself by stopping one group from smoking and letting an otherwise identical group smoke, for example.

Independence between these random variables implies that the covariance between them is 0. The GMM/SMM estimate is taken as the value of the causal parameter, which equates the sample covariance between these variables in the data set to 0. We refer to the sample covariance as an "estimating function" rather than an objective function, because it is equated to 0 and not maximized or minimized.

It is advisable to conduct a sensitivity analysis to assess the impact of these parameter values on the causal estimates.

Thus John's saying "hello" may refer to the same event as John's saying "hello" loudly, but substituting the one description for the other may still change the truth-value of the causal claim.

For example, in the case of pre-primary education, using a uniform prior distribution ranging from (-infty) to (+infty) would say that all values of the causal effect across the real numbers are equally likely.

The model p-value of the causal model is p = 0.75 and the RMSEA is ≤ 0.001.

We calculated the LEO.NB.SingleMarker (LEO) score, which is a relative fitting index that compares the model fitting p-value of the causal model for a gene x i causing ME to that of the next best competing model.

For the single marker score LEO.NB.SingleMarker, we use a threshold of 1, which implies that the model p-value of the causal model is 101 = 10 fold higher than that of the next best model.

Of these, 20 combinations have a strikingly high LEO score of 3 or higher; for most of these 20 combinations, the model fitting p-value of the causal model SNP → Methylation → Expression is above 0.01, indicating a good fit and lending further credence to these results (Additional file 4: Table S3).

However, since the estimated values of the causal effects were similar with model M1 and the two derived models, results from these models are not presented.

By fostering the interplay between observation on large sets of natural scene fixation data, and experimental designs that manipulate scene statistics, the proposed approach has the potential to pave the way from correlations between fixation probability and feature values to the causal effect of features.