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.

Discussion of “Estimation of Intervention Effects with Noncompliance: Alternative Model Specifications” by Booil Jo:

Abstract: We thank the editors for the opportunity to offer our comments on this very readable article on recent work on noncompliance. Since Angrist, Imbens, and Rubin (1996), there has been an explosion of interest and activities in noncompliance related methods and applications, which include the bridging of work in different fields such as statistics, economics, epidemiology, sociology, and education. This article contributes nicely to this expanding literature by explicating various assumptions involving covariates that can be used to uniquely identify maximum likelihood estimates in place of exclusion restrictions. Although we like the article very much, as discussants we focus on points designed to stimulate further discussion. Thus the casual reader may get the mistaken impression that we are more critical than we really are. The topics we address are: (a) summarizing results by "significant" versus "not significant"; (b) handling missing data by listwise deletion; (c) describing subpopulation differences as "impacts" or "effects"; (d) discussing the scientific plausibility of competing models.

Assessing the Potential Impact of a Nationwide Class-Based Affirmative Action System

Abstract: We examine the possible consequences of a change in law school admissions in the United States from an affirmative action system based on race to one based on socioeconomic class. Using data from the 1991-1996 Law School Admission Council Bar Passage Study, students were reassigned attendance by simulation to law school tiers by transferring the affirmative action advantage for black students to students from low socioeconomic backgrounds. The hypothetical academic outcomes for the students were then multiply-imputed to quantify the uncertainty of the resulting estimates. The analysis predicts dramatic decreases in the numbers of black students in top law school tiers, suggesting that class-based affirmative action is insufficient to maintain racial diversity in prestigious law schools. Furthermore, there appear to be no statistically significant changes in the graduation and bar passage rates of students in any demographic group. The results thus provide evidence that, other than increasing their representation in upper tiers, current affirmative action policies relative to a socioeconomic-based system neither substantially help nor harm minority academic outcomes, contradicting the predictions of the "mismatch" hypothesis, which asserts otherwise.

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.