Donald B. Rubin

Bio: Donald B. Rubin is an academic researcher from Tsinghua University. The author has contributed to research in topics: Causal inference & Missing data. The author has an hindex of 132, co-authored 515 publications receiving 262632 citations. Previous affiliations of Donald B. Rubin include University of Chicago & Harvard University.

Combining Panel Data Sets with Attrition and Refreshment Samples

Abstract: This discussion paper resulted in a publication in Econometrica (2001). Volume 69, issue 6, pages 1645-1659. With panel data important issues can be resolved that can not beaddressed with cross--sectional data. A major drawback is that paneldata suffer from more severe missing data problems. Adding a sampleconsisting of new units randomly drawn from the original populationas replacements for units who have dropped out of the panel, aso--called refreshment sample, can be helpful in mitigating theeffects of attrition, both by allowing for estimation of richermodels and by making estimation of conventional models moreprecise. In this paper we develop a family of models thatincorporate refreshment samples, and we demonstrate in an applicationto a Dutch data set on travel behaviour that such models can lead tosubstantially different results than models that assume that themissing data process is ignorable or conventional econometric modelsfor panel data with attrition.

The practical importance of understanding placebo effects and their role when approving drugs and recommending doses for medical practice

Abstract: The general reliance on blinded placebo-controlled randomized trials, both to approve drugs and to set their recommended dosages, although statistically sound for some purposes, may be statistically naive in the context of guiding general medical practice. Briefly, the reason is that medical prescriptions are unblinded, and so patients who receive drugs in practice receive both the active medical effect of the drug, as estimated in blinded trials, as well as any “placebo effects”, rarely carefully defined or estimated, but intuitively defined as the extra effect, on “you”, when you think you are being actively treated, even when in fact you may not be receiving anything that actually works.

Contrast-specific propensity scores

Abstract: Basic propensity score methodology is designed to balance the distributions of multivariate pre-treatment covariates when comparing one active treatment with one control treatment. However, practic...

Fitting Linear Mixed-Effects Models Using lme4

Abstract: Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.

Deep Learning

Abstract: Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Latent dirichlet allocation

Abstract: We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.