gov3009-l

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717 discussions

HIV: Estimating the causal effect of dynamic treatment regimes in observational studies

by Anders Schwartz Corr

Estimating the causal effect of dynamic treatment regimes in observational studies. Miguel Hernan, MD, Instructor in Epidemiology, Harvard School of Public Health. Joint work with James Robins and Stephen Cole Wednesday November 13 at noon Center for Basic Research in the Social Sciences 34 Kirkland Street, Room 22 Lunch will be served. Abstract: Large randomized experiments with full adherence and no losses to follow-up can be used to make causal inferences without further assumptions. These ideal experiments may be used to evaluate the effect of treatment regimes that a) fully describe each subject's treatment experience during the study even before the study starts (e.g., always treat, never treat, treat every other month), or b) do not fully describe each subject's treatment experience during the study before the study starts because the treatment actually received depends on the subject's time-varying characteristics (e.g., treat until side effects appear, treat only when some blood parameter reaches the value 200). We call the latter dynamic treatment regimes. The analysis of ideal experiments is straightforward: simply compare the mean outcome (or survival) in subjects under each (dynamic or non-dynamic) treatment regime. Observational studies can be used to make causal inferences only under the assumption that they are able to provide the same results as a (possibly hypothetical) randomized experiment. This assumption, also known as "the assumption of no unmeasured confounders" or "of sequential randomization", is not testable. But even if this assumption holds, and in the absence of model misspecification, the standard analysis of observational studies with time-varying treatments may yield estimates that cannot be endowed with a causal interpretation. This limitation of standard methods applies to the estimation of the effects of both dynamic and non-dynamic treatment regimes. In contrast, methods based on marginal structural models and structural nested models would yield valid causal estimates. We have previously applied a marginal structural Cox model to estimate the effects of non-dynamic treatments in HIV/AIDS epidemiology. The subject-matter question we addressed was "should HIV-infected individuals be treated with highly active antiretroviral therapy?" In this talk, I will review our previous results and describe the application of a structural nested accelerated failure time model for estimating the effect of dynamic treatments among HIV-infected individuals. The question is "should treatment with highly active antiretroviral therapy start when CD4 count drops to 200 or to 350?" Also, I will discuss computational issues and different methods to handle censoring. When the paper becomes available, a link will be sent to those requesting it (email corr(a)fas.harvard.edu) and to those on the workshop email list. Those unfamiliar with methods for causal inference from observational data may find it useful to read about estimating non-dynamic treatments as described in http://www.hsph.harvard.edu/causal/publications/joint-causal.pdf The Research Workshop in Applied Statistics is a forum for graduate students, faculty, and visiting scholars to present and discuss statistical innovations and applications in the social sciences. For more information or to subscribe to the permanent workshop list, email corr(a)fas.harvard.edu.

21 years, 6 months

Paper Available: Internet Stocks and Market Classification

by Anders Schwartz Corr

Paper now available at: http://www.courses.fas.harvard.edu/~gov3009/handouts/comovementinternet.pdf Shrewd, Crude, or Simply Deluded? Market Classification and the Internet Stock Phenomenon. Ezra Zuckerman (MIT, Sloan School of Management) Wednesday November 6 at noon Center for Basic Research in the Social Sciences 34 Kirkland Street, Room 22 Lunch will be served. Abstract: We analyze comovement among Internet and other categories of stocks during the late 1990s and 2000 in an effort to assess the sophistication of stock-market valuation. Prominent accounts of the Internet stock phenomenon suggest that the prices of these stocks were determined by simplistic thinking. In particular, investors were not discriminating as they crudely grouped all Internet stocks into an undifferentiated and highly attractive investment category. We find that, in fact, comovement among Internet stocks was high throughout much of this period but did not reach the very high levels assumed by prevailing accounts. In addition, we describe two additional patterns that are problematic for such interpretations. First, comovement is less characteristic of price increases than of price drops. Second, Internet stocks exhibited moderate to high period-to-period consistency in the manner by which category members were distinguished by investors. Together, our evidence supports our view that, rather than being anomalous, the Internet stock phenomenon was symptomatic of the general way by which equity prices are determined: valuation is driven by prevailing theories of value, which are reasonable but quite fallible. This view has important implications for how scholars and managers understand and react to stock market dynamics. The paper will be posted as soon as it becomes available on the workshop website, www.courses.fas.harvard.edu/~gov3009/fall02/ The Research Workshop in Applied Statistics is a forum for graduate students, faculty, and visiting scholars to present and discuss statistical innovations and applications in the social sciences. For more information, contact corr(a)fas.harvard.edu.

21 years, 6 months

Internet Stocks and Market Classification

by Anders Schwartz Corr

Shrewd, Crude, or Simply Deluded? Market Classification and the Internet Stock Phenomenon. Ezra Zuckerman (MIT, Sloan School of Management) Wednesday November 6 at noon Center for Basic Research in the Social Sciences 34 Kirkland Street, Room 22 Lunch will be served. Abstract: We analyze comovement among Internet and other categories of stocks during the late 1990s and 2000 in an effort to assess the sophistication of stock-market valuation. Prominent accounts of the Internet stock phenomenon suggest that the prices of these stocks were determined by simplistic thinking. In particular, investors were not discriminating as they crudely grouped all Internet stocks into an undifferentiated and highly attractive investment category. We find that, in fact, comovement among Internet stocks was high throughout much of this period but did not reach the very high levels assumed by prevailing accounts. In addition, we describe two additional patterns that are problematic for such interpretations. First, comovement is less characteristic of price increases than of price drops. Second, Internet stocks exhibited moderate to high period-to-period consistency in the manner by which category members were distinguished by investors. Together, our evidence supports our view that, rather than being anomalous, the Internet stock phenomenon was symptomatic of the general way by which equity prices are determined: valuation is driven by prevailing theories of value, which are reasonable but quite fallible. This view has important implications for how scholars and managers understand and react to stock market dynamics. The paper will be posted as soon as it becomes available on the workshop website, www.courses.fas.harvard.edu/~gov3009/fall02/ The Research Workshop in Applied Statistics is a forum for graduate students, faculty, and visiting scholars to present and discuss statistical innovations and applications in the social sciences. For more information, contact corr(a)fas.harvard.edu.

21 years, 6 months

Black-White Differences in the Causal Effect of Expectations on Attainment

by Anders Schwartz Corr

Methodologist as Arbitrator: Five Models for Black-White Differences in the Causal Effect of Expectations on Attainment. Professor Stephen L. Morgan (Sociology, Cornell University) Wednesday October 30 at noon Center for Basic Research in the Social Sciences 34 Kirkland Street, Room 22 Lunch will be served. Abstract: When progress in applied research slows because opposing coalitions of investigators privilege their favored models, methodologists can contribute by addressing a tractable unresolved question that is relevant to all competing positions. In this article the literature on educational attainment is addressed broadly by focusing on alternative positions on the need to model students' own beliefs and more narrowly by attempting to answer a classic question that emerged in debates over the power of status attainment approaches: Why is the relationship between educational expectations and subsequent educational attainment weaker for blacks than for whites? Analyzing the High School & Beyond data, five complementary models of the causal effect of expectations on attainment are offered: a traditional path-model, an average effects instrumental variable model, a counterfactual analysis of bounds, a rational expectations forecasting model, and a panel data model of updated expectations. The general methodological goal is to demonstrate how multi-model research can be pursued: After delineating the motivating assumptions of alternative theoretical positions and the explanations that they sustain, one then simultaneously estimates a wide range of plausible models, derives alternative permissible conclusions, and specifies the implied research program that must be mounted to choose from among the plausible conclusions. The paper may be accessed at: http://www.courses.fas.harvard.edu/~gov3009/handouts/methodologistoct2002.p… The Research Workshop in Applied Statistics is a forum for graduate students, faculty, and visiting scholars to present and discuss statistical innovations and applications in the social sciences. For more information or to receive workshop announcements every week, contact corr(a)fas.harvard.edu.

21 years, 6 months

Randomness and Coincidences: Reconciling Intuition and Probability Theory

by Anders Schwartz Corr

Randomness and Coincidences: Reconciling Intuition and Probability Theory Presentation by Tom Griffiths (Stanford University, Psychology Department). Coauthored by Joshua Tennenbaum (Stanford University, Psychology Department). Wednesday October 23 at noon Center for Basic Research in the Social Sciences 34 Kirkland Street, Room 22 Lunch will be served. Abstract: People are notoriously bad at reasoning about chance. Our intuitions about randomness and coincidences seem to be inconsistent with the normative structure of statistics: events that are equally likely to arise by chance differ in their subjective randomness, and we consistently underestimate the probability of coincidences. I will argue that we can understand why these apparent errors arise by focusing on the evidence observations provide about the processes that generated them rather than their probability of occurring by chance. As part of this argument, I will present a simple Bayesian framework that gives predictions about both the extent to which a set of events will seem random, and the strength of coincidences. This framework accurately predicts behavior in a variety of contexts, suggesting that statistics might yet contribute to understanding our intuitions about chance. The Research Workshop in Applied Statistics is a forum for graduate students, faculty, and visiting scholars to present and discuss statistical innovations and applications in the social sciences. For more information, contact corr(a)fas.harvard.edu. The paper may be accessed at: http://www-psych.stanford.edu/~gruffydd/papers/random.pdf The presenter's email is: gruffydd(a)psych.stanford.edu

21 years, 7 months

Applied Statistics Seminar Announcement

by Jasjeet Singh Sekhon

"Geography, Power, and the Size of Nations: An Agent-Based Model of the International System" Presentation by Anders Corr (Harvard University, Government Department) Wednesday October 16 at noon Center for Basic Research in the Social Sciences 34 Kirkland Street, Room 22 Lunch will be served. The Research Workshop in Applied Statistics is a forum for graduate students, faculty, and visiting scholars to present and discuss statistical innovations and applications in the social sciences. For more information, contact corr(a)fas.harvard.edu. Abstract: Power discrepancy is an observed characteristic of the international system. Lars-Erik Cederman's agent-based computer model, GeoSim, simulates the interaction of states in an international system, but produces equilibria in which power is distributed homogenously over system members (Cederman 2002). Alesina and Spolaore (forthcoming) likewise model changes in the size of nations without accounting for equilibria in which small and large states coexist. This study enriches Cederman's model by adding geography, thereby supplying a missing link in both literatures. As expected, geography yields a long-run distribution of power consistent with the empirical discrepancy between states, including the simultaneous existence of small and hegemonic polities. The paper may be accessed at: http://www.courses.fas.harvard.edu/~gov3009/handouts/geosimgeog8.pdf

21 years, 7 months

Sequential Decision Strategies and Structural Nested Methods

by Anders Schwartz Corr

``Estimation of Optimal Treatment or Sequential Decision Strategies from Non-Experimental Data Using Structural Nested Models" Presentation by Professor James Robins (Harvard University, School of Public Health) Wednesday October 9 at noon Center for Basic Research in the Social Sciences 34 Kirkland Street, Room 22 Lunch will be served. The Research Workshop in Applied Statistics is a forum for graduate students, faculty, and visiting scholars to present and discuss statistical innovations and applications in the social sciences. For more information, contact corr(a)fas.harvard.edu. This week's paper is not available prior to the presentation.

21 years, 7 months

Voter Mobilization and Field Experiments

by Anders Schwartz Corr

"The importance of statistical methodology for analyzing data from field experimentation: Evaluating voter mobilization strategies" by Kosuke Imai (Government, Harvard University) The first presentation of the Research Workshop in Applied Statistics will be held on Wednesday 9/25 at noon, CBRSS Rm. 22. Lunch will be served. The paper may be accessed at: http://www.courses.fas.harvard.edu/~gov3009/fall02/ Abstract: Field experimentation is making its way back into the toolkit of political scientists. Gerber and Green have led this important methodological development that is likely to improve causal inferences in political science research. However, they believe that field experiments only require "rudimentary data analysis." Countering this claim, I use Gerber and Green's voter mobilization data (2000) to show that statistical methods are essential to address complications that invariably arise in field experiments. I demonstrate how incomplete randomization of treatment assignment led to the authors' puzzling finding that get-out-the-vote calls discourage voters from going to the polls, reducing turnout by 5 percent. An application of matching, which is more appropriate given the incomplete randomization, reveals that telephone canvassing increases turnout by about 5 percent. My analysis also finds that mail canvassing is a signficant cost-effective alternative, and that appeals related to civic engagement are more effective than the original analysis indicated. Contact: corr(a)fas.harvard.edu

21 years, 7 months

Research Workshop in Applied Statistics

by Anders Schwartz Corr

*************************************** Research Workshop in Applied Statistics *************************************** Jasjeet Sekhon (Government) Garrett FitzMaurice (School of Public Health) Lee Fleming (Business School) Gary King (Government) Donald Rubin (Statistics) Christopher Winship (Sociology) The Research Workshop in Applied Statistics is a forum for graduate students, faculty, and visiting scholars to present and discuss work in progress and exchange ideas. It is intended as a tour of Harvard's statistical innovations and applications with weekly stops in different disciplines such as economics, epidemiology, medicine, political science, psychology, public policy, public health, sociology and statistics. The topics of papers presented in previous years included missing data, survey analysis, Bayesian simulation, sample selection, and models for election and portfolio choice. Faculty and student participants in the workshop present their current projects, and guest speakers also give occasional presentations. The workshop provides an excellent opportunity for informal interaction between graduate students and faculty from a variety of disciplines. Course credit is available for students as Government 3009. Lunch is provided. If you are interested, please be sure to attend our first meeting on Wednesday, September 18th at noon, in Room 22, Center for Basic Research in Social Sciences (CBRSS, 34 Kirkland St., this is the yellow building across the street from William James Hall). Contact information and previous presentations may be found at the course web site: http://www.courses.fas.harvard.edu/~gov3009/ To join the gov3009 mailing list, send e-mail to gov3009-l-request(a)fas.harvard.edu with the following text message: subscribe end Questions? Please contact the workshop coordinator, Anders Corr, at corr(a)fas.harvard.edu ********************************************************************* *********************************************************************

21 years, 8 months

Applied Statistics Workshop

by Shigeo Hirano

Hi Everyone, This Wednesday, April 24th, David van Dyk and Kosuke Imai will be presenting their paper entitled, "Causal inference with general treatment regimes". The paper can be downloaded from our course page (http://www.courses.fas.harvard.edu/~gov3009/spring02) or from (http://www.people.fas.harvard.edu/~kimai/files/pscore.ps). See you all Wednesday! Shigeo __________________________________________________________________ Shigeo Hirano Political Economy and Government shirano(a)fas.harvard.edu Harvard University

22 years

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