Author
Stephen L. Morgan
Other affiliations: Harvard University, Cornell University
Bio: Stephen L. Morgan is an academic researcher from Johns Hopkins University. The author has contributed to research in topics: Causal inference & Educational attainment. The author has an hindex of 28, co-authored 73 publications receiving 8179 citations. Previous affiliations of Stephen L. Morgan include Harvard University & Cornell University.
Papers published on a yearly basis
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30 Jul 2007TL;DR: In this article, the authors proposed a method to estimate causal effects by conditioning on observed variables to block backdoor paths in observational social science research, but the method is limited to the case of causal exposure and identification criteria for conditioning estimators.
Abstract: Part I. Causality and Empirical Research in the Social Sciences: 1. Introduction Part II. Counterfactuals, Potential Outcomes, and Causal Graphs: 2. Counterfactuals and the potential-outcome model 3. Causal graphs Part III. Estimating Causal Effects by Conditioning on Observed Variables to Block Backdoor Paths: 4. Models of causal exposure and identification criteria for conditioning estimators 5. Matching estimators of causal effects 6. Regression estimators of causal effects 7. Weighted regression estimators of causal effects Part IV. Estimating Causal Effects When Backdoor Conditioning Is Ineffective: 8. Self-selection, heterogeneity, and causal graphs 9. Instrumental-variable estimators of causal effects 10. Mechanisms and causal explanation 11. Repeated observations and the estimation of causal effects Part V. Estimation When Causal Effects Are Not Point Identified by Observables: 12. Distributional assumptions, set identification, and sensitivity analysis Part VI. Conclusions: 13. Counterfactuals and the future of empirical research in observational social science.
1,701 citations
University of Southern California1, Duke University2, Stockholm School of Economics3, Center for Open Science4, University of Virginia5, University of Amsterdam6, University of Pennsylvania7, University of North Carolina at Chapel Hill8, University of Regensburg9, California Institute of Technology10, Research Institute of Industrial Economics11, New York University12, Cardiff University13, Northwestern University14, Mathematica Policy Research15, Ohio State University16, University of Sussex17, Texas A&M University18, Royal Holloway, University of London19, University of Zurich20, University of Melbourne21, University of Wisconsin-Madison22, University of Michigan23, Stanford University24, Rutgers University25, Columbia University26, University of Washington27, University of Edinburgh28, National University of Singapore29, Utrecht University30, Arizona State University31, Princeton University32, University of California, Los Angeles33, Imperial College London34, University of Innsbruck35, Harvard University36, University of Chicago37, University of Pittsburgh38, University of Notre Dame39, University of California, Berkeley40, Johns Hopkins University41, University of Bristol42, University of New South Wales43, Dartmouth College44, Whitman College45, University of Puerto Rico46, University of Milan47, University of California, Irvine48, Paris Dauphine University49, University of British Columbia50, Ludwig Maximilian University of Munich51, Purdue University52, Washington University in St. Louis53, University of California, Davis54, Microsoft55
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
Abstract: We propose to change the default P-value threshold for statistical significance from 0.05 to 0.005 for claims of new discoveries.
1,586 citations
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TL;DR: This article proposed to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005, which is the threshold used in this paper.
Abstract: We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.
1,415 citations
TL;DR: In this article, the authors review the large literature produced primarily by statisticians and econometricians in the past two decades on the estimation of causal effects from observational data and present estimators that exploit the additional information furnished by longitudinal data.
Abstract: ▪ Abstract When experimental designs are infeasible, researchers must resort to the use of observational data from surveys, censuses, and administrative records. Because assignment to the independent variables of observational data is usually nonrandom, the challenge of estimating causal effects with observational data can be formidable. In this chapter, we review the large literature produced primarily by statisticians and econometricians in the past two decades on the estimation of causal effects from observational data. We first review the now widely accepted counterfactual framework for the modeling of causal effects. After examining estimators, both old and new, that can be used to estimate causal effects from cross-sectional data, we present estimators that exploit the additional information furnished by longitudinal data. Because of the size and technical nature of the literature, we cannot offer a fully detailed and comprehensive presentation. Instead, we present only the main features of methods ...
873 citations
TL;DR: For example, this paper found that for public schools, social closure among parents is negatively associated with achievement gains in mathematics, net of friendship density among students, and this evidence of a negative effect of parental social closure within the public school sector lends support to their alternative hypothesis that horizon-expanding schools foster more learning than do norm enforcing schools.
Abstract: Through an analysis of gains in mathematics achievement between the tenth and twelfth grades for respondents to the National Education Longitudinal Study of 1988, we examine Coleman's explanation for why Catholic schools apparently produce more learning than public schools. According to Coleman, Catholic schools benefit from larger endowments of social capital, generated in part through greater intergenerational social closure (i.e., dense network connections between the parents of students). Instead, we find that for public schools, social closure among parents is negatively associated with achievement gains in mathematics, net of friendship density among students. This evidence of a negative effect of parental social closure within the public school sector lends support to our alternative hypothesis that horizon-expanding schools foster more learning than do norm-enforcing schools. Moreover, this result renders social closure incapable of explaining any portion of the Catholic school effect on learning, even though within the Catholic school sector there is some evidence that social closure is positively associated with learning
473 citations
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01 Jan 2001TL;DR: In this article, the authors present experiments and generalized Causal inference methods for single and multiple studies, using both control groups and pretest observations on the outcome of the experiment, and a critical assessment of their assumptions.
Abstract: 1. Experiments and Generalized Causal Inference 2. Statistical Conclusion Validity and Internal Validity 3. Construct Validity and External Validity 4. Quasi-Experimental Designs That Either Lack a Control Group or Lack Pretest Observations on the Outcome 5. Quasi-Experimental Designs That Use Both Control Groups and Pretests 6. Quasi-Experimentation: Interrupted Time Series Designs 7. Regression Discontinuity Designs 8. Randomized Experiments: Rationale, Designs, and Conditions Conducive to Doing Them 9. Practical Problems 1: Ethics, Participant Recruitment, and Random Assignment 10. Practical Problems 2: Treatment Implementation and Attrition 11. Generalized Causal Inference: A Grounded Theory 12. Generalized Causal Inference: Methods for Single Studies 13. Generalized Causal Inference: Methods for Multiple Studies 14. A Critical Assessment of Our Assumptions
12,215 citations
TL;DR: The propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects, and different causal average treatment effects and their relationship with propensity score analyses are described.
Abstract: The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.
7,895 citations
TL;DR: A structure for thinking about matching methods and guidance on their use is provided, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
Abstract: When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970's, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine, and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods-or developing methods related to matching-do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
3,952 citations
01 Jan 2002
TL;DR: This article investigated whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997) with negative results.
Abstract: We investigate whether income inequality affects subsequent growth in a cross-country sample for 1965-90, using the models of Barro (1997), Bleaney and Nishiyama (2002) and Sachs and Warner (1997), with negative results. We then investigate the evolution of income inequality over the same period and its correlation with growth. The dominating feature is inequality convergence across countries. This convergence has been significantly faster amongst developed countries. Growth does not appear to influence the evolution of inequality over time. Outline
3,770 citations