Showing papers by "Donald B. Rubin published in 1978"

Bayesian Inference for Causal Effects: The Role of Randomization

Abstract: Causal effects are comparisons among values that would have been observed under all possible assignments of treatments to experimental units. In an experiment, one assignment of treatments is chosen and only the values under that assignment can be observed. Bayesian inference for causal effects follows from finding the predictive distribution of the values under the other assignments of treatments. This perspective makes clear the role of mechanisms that sample experimental units, assign treatments and record data. Unless these mechanisms are ignorable (known probabilistic functions of recorded values), the Bayesian must model them in the data analysis and, consequently, confront inferences for causal effects that are sensitive to the specification of the prior distribution of the data. Moreover, not all ignorable mechanisms can yield data from which inferences for causal effects are insensitive to prior specifications. Classical randomized designs stand out as especially appealing assignment mechanisms designed to make inference for causal effects straightforward by limiting the sensitivity of a valid Bayesian analysis.

Interpersonal expectancy effects: the first 345 studies

Abstract: The research area of interpersonal expectancy effects originally derived from a general consideration of the effects of experimenters on the results of their research. One of these is the expectancy effect, the tendency for experimenters to obtain results they expect, not simply because they have correctly anticipated nature's response but rather because they have helped to shape that response through their expectations. When behavioral researchers expect certain results from their human (or animal) subjects they appear unwittingly to treat them in such a way as to increase the probability that they will respond as expected.In the first few years of research on this problem of the interpersonal (or interorganism) self-fulfilling prophecy, the “prophet” was always an experimenter and the affected phenomenon was always the behavior of an experimental subject. In more recent years, however, the research has been extended from experimenters to teachers, employers, and therapists whose expectations for their pupils, employees, and patients might also come to serve as interpersonal self-fulfilling prophecies.Our general purpose is to summarize the results of 345 experiments investigating interpersonal expectancy effects. These studies fall into eight broad categories of research: reaction time, inkblot tests, animal learning, laboratory interviews, psychophysical judgments, learning and ability, person perception, and everyday life situations. For the entire sample of studies, as well as for each specific research area, we (1) determine the overall probability that interpersonal expectancy effects do in fact occur, (2) estimate their average magnitude so as to evaluate their substantive and methodological importance, and (3) illustrate some methods that may be useful to others wishing to summarize quantitatively entire bodies of research (a practice that is, happily, on the increase).

Bias reduction using mahalanobis metric matching

Abstract: SUMMARY Monte Carlo methods are used to study the ability of nearest available Mahalanobis metric matching to make the means of matching variables more similar in matched samples than in random samples.

Using multivariate matched sampling and regression adjustment to control bias in observational studies

Abstract: Monte Carlo methods are used to study the efficacy of multivariate matched sampling and regression adjustment for controlling bias due to specific matching variables when dependent variables are moderately nonlinear in . The general conclusion is that nearest available Mahalanobis metric matching in combination with regression adjustment on matched pair differences is a highly effective plan for controlling bias due to .

Relating tests given to different samples

Abstract: Suppose a collection of standard tests is given to all subjects in a random sample, but a different new test is given to each group of subjects in nonoverlapping subsamples. A simple method is developed for displaying the information that the data set contains about the correlational structure of the new tests. This is possible to some extent, even though each subject takes only one new test. The method uses plausible values of the partial correlations among the new tests given the standard tests in order to generate plausible simple correlations among the new tests and plausible multiple correlations between composites of the new tests and the standard tests. The real data example included suggests that the method can be useful in practical problems.

A Note on Bayesian, Likelihood, and Sampling Distribution Inferences

Abstract: A simple example is presented that illustrates advantages of Bayesian and likelihood methods of inference relative to sampling distribution methods of inference. It is argued that Bayesian and likelihood methods of inference should be utilized more generally to analyze real data. Sampling distributions should be used to evaluate the long term performance of procedures.