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BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190508T120000
DTEND;TZID=America/Los_Angeles:20190508T133000
DTSTAMP:20260430T105229
CREATED:20190426T154158Z
LAST-MODIFIED:20190426T154158Z
UID:2058-1557316800-1557322200@css.stat.ucla.edu
SUMMARY:Brandon Stewart\, Princeton University
DESCRIPTION:Title: How to Make Causal Inferences Using Texts \nAbstract: Texts are increasingly used to make causal inferences: either with the document serving as the treatment or the outcome. We introduce a new conceptual framework to understand all text-based causal inferences\, demonstrate fundamental problems that arise when using manual or computational approaches applied to text for causal inference\, and provide solutions to the problems we raise.  We demonstrate that all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation.  Estimating this latent representation\, however\, creates new risks: we may unintentionally create a dependency across observations or create opportunities to fish for large effects.  To address these risks\, we introduce a train/test split framework and apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness.  Our work provides a rigorous foundation for text-based causal inferences\, connecting two previously disparate literatures. (Joint Work with Egami\, Fong\, Grimmer and Roberts)
URL:https://css.stat.ucla.edu/event/brandon-stewart-princeton-university/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States
CATEGORIES:css seminar,Divisional Publish
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190501T120000
DTEND;TZID=America/Los_Angeles:20190501T133000
DTSTAMP:20260430T105229
CREATED:20190426T153944Z
LAST-MODIFIED:20190426T153944Z
UID:2053-1556712000-1556717400@css.stat.ucla.edu
SUMMARY:Susan Athey\, Stanford University
DESCRIPTION:Title: “Estimating Heterogeneous Treatment Effects and Optimal Treatment Assignment Policies” \nAbstract: This talk will review recently developed methods for estimating conditional average treatment effects and optimal treatment assignment policies in experimental and observational studies\, including settings with unconfoundedness or instrumental variables.  Multi-armed bandits for learning treatment assignment policies will also be considered.
URL:https://css.stat.ucla.edu/event/susan-athey-stanford-university/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States
CATEGORIES:css seminar,Divisional Publish
ATTACH;FMTTYPE=image/jpeg:https://css.stat.ucla.edu/wp-content/uploads/sites/67/2019/04/susan-athey.jpg
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190312T140000
DTEND;TZID=America/Los_Angeles:20190312T153000
DTSTAMP:20260430T105229
CREATED:20190228T181841Z
LAST-MODIFIED:20190228T181841Z
UID:1621-1552399200-1552404600@css.stat.ucla.edu
SUMMARY:Eloise Kaizar\,  Ohio State University
DESCRIPTION:Eloise Kaizar\, Ohio State University \nRandomized controlled trials are often thought to provide definitive evidence on the magnitude of treatment effects. But because treatment modifiers may have a different distribution in a real world population than among trial participants\, trial results may not directly reflect the average treatment effect that would follow real world adoption of a new treatment. Recently\, weight-based methods have been repurposed to more provide more relevant average effect estimates for real populations. In this talk\, I summarize important analytical choices involving what should and should not be borrowed from other applications of weight-based estimators\, make evidence-based recommendations about confidence interval construction\, and present conjectures about best choices for other aspects of statistical inference. \nEloise Kaizar is Associate Professor of Statistics at The Ohio State University. Her primary research focus is on assessing the effects and safety of medical exposures and interventions\, especially those whose effects are heterogeneous across populations or measured with rare event outcomes. As such\, she has worked on methodology to combine multiple sources of information relevant to the same broad policy or patient-centered question. She is particularly interested in how data collected via different study designs can contribute complementary information.
URL:https://css.stat.ucla.edu/event/eloise-kaizar-ohio-state-university/
LOCATION:1434A Physics and Astronomy\, 1434A Physics and Astronomy\, Los Angeles\, CA\, 90098\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190305T140000
DTEND;TZID=America/Los_Angeles:20190305T153000
DTSTAMP:20260430T105229
CREATED:20190228T181445Z
LAST-MODIFIED:20190302T182035Z
UID:1617-1551794400-1551799800@css.stat.ucla.edu
SUMMARY:Lan Liu\, University of Minnesota at Twin Cities
DESCRIPTION:Lan Liu\, University of Minnesota at Twin Cities\n“Parsimonious Regressions for Repeated Measure Analysis” \nAbstract: Longitudinal data with repeated measures frequently arises in various\ndisciplines. The standard methods typically impose a mean outcome model as\na function of individual features\, time and their interactions. However\,\nthe validity of the estimators relies on the correct specifications of the\ntime dependency. The envelope method is recently proposed as a sufficient\ndimension reduction (SDR) method in multivariate regressions. In this\npaper\, we demonstrate the use of the envelope method as a new parsimonious\nregression method for repeated measures analysis\, where the specification\nof the underlying pattern of time trend is not required by the model. We\nfound that if there is enough prior information to support the\nspecification of the functional dependency of the mean outcome on time and\nif the dimension of the prespecified functional form is low\, then the\nstandard method is advantageous as an efficient and unbiased estimator.\nOtherwise\, the envelope method is appealing as a more robust and\npotentially efficient parsimonious regression method in repeated measure\nanalysis. We compare the performance of the envelope estimators with the\nexisting estimators in simulation study and in an application to the China\nHealth and Nutrition Survey
URL:https://css.stat.ucla.edu/event/lan-liu-university-of-minnesota-at-twin-cities/
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190226T140000
DTEND;TZID=America/Los_Angeles:20190226T153000
DTSTAMP:20260430T105229
CREATED:20190228T181503Z
LAST-MODIFIED:20190228T181503Z
UID:1616-1551189600-1551195000@css.stat.ucla.edu
SUMMARY:Adeline Lo\, Princeton University
DESCRIPTION:Adeline Lo\, Princeton University \nAbstract: High dimensional (HD) data\, where the number of covariates and/or meaningful covariate interactions might exceed the number of observations\, is increasing used in prediction in the social sciences. An important question for the researcher is how to select the most predictive covariates among all the available covariates. Common covariate selection approaches use ad hoc rules to remove noise covariates\, or select covariates through the criterion of statistical significance or by using machine learning techniques. These can suffer from lack of objectivity\, choosing some but not all predictive covariates\, and failing reasonable standards of consistency that are expected to hold in most high-dimensional social science data. The literature is scarce in statistics that can be used to directly evaluate covariate predictivity. We address these issues by proposing a variable screening step prior to traditional statistical modeling\, in which we screen covariates for their predictivity. We propose the influence (I) statistic to evaluate covariates in the screening stage\, showing that the statistic is directly related to predictivity and can help screen out noisy covariates and discover meaningful covariate interactions. We illustrate how our screening approach can removing noisy phrases from U.S. Congressional speeches and rank important ones to measure partisanship. We also show improvements to out-of-sample forecasting in a state failure application. Our approach is applicable via an open-source software package. \nAdeline Lo is a postdoctoral research associate at the Department of Politics at Princeton University. Her research lies in the design of statistical tools for prediction and measurement for applied social sciences\, with a substantive interest in conflict and post-conflict processes. She has an ongoing research agenda on high dimensional forecasting\, especially in application to violent events. Her work has been published in the Proceedings of the National Academy of Sciences\, Comparative Political Studies and Nature. She will be joining the Department of Political Science at the University of Wisconsin-Madison as an Assistant Professor in Fall 2019.
URL:https://css.stat.ucla.edu/event/adeline-lo-princeton-university-2/
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190123T120000
DTEND;TZID=America/Los_Angeles:20190123T133000
DTSTAMP:20260430T105229
CREATED:20190119T195942Z
LAST-MODIFIED:20190122T214513Z
UID:1585-1548244800-1548250200@css.stat.ucla.edu
SUMMARY:Kosuke Imai\, Harvard University
DESCRIPTION:Title:  Matching Methods for Causal Inference with Time-Series Cross-Section Data\nAbstract:  Matching methods aim to improve the validity of causal inference in observational studies by reducing model dependence and offering intuitive diagnostics. While they have become a part of standard tool kit for empirical researchers across disciplines\, matching methods are rarely used when analyzing time-series cross-section (TSCS) data\, which consist of a relatively large number of repeated measurements on the same units. We develop a methodological framework that enables the application of matching methods to TSCS data. In the proposed approach\, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated observation has outcome and covariate histories similar to those of its matched control observations. Assessing the quality of matches is done by examining covariate balance. After the refinement\, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator\, accounting for a time trend. We also show that the proposed matching estimator can be written as a weighted linear regression estimator with unit and time fixed effects\, providing model-based standard errors. We illustrate the proposed methodology by estimating the causal effects of democracy on economic growth\, as well as the impact of inter-state war on inheritance tax. The open-source software is available for implementing the proposed matching methods.\nCo-sponsored with the Political Science Department\, Statistics Department and the Center for Social Statistics\nMore on Prof. Imai
URL:https://css.stat.ucla.edu/event/kosuke-imai-harvard-university-2/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 5201 Sumner Ave\, United States\, 101 C St\, United States\, 301 C St\, United States
CATEGORIES:Ccpr Seminar,css seminar,Divisional Publish
ATTACH;FMTTYPE=image/png:https://css.stat.ucla.edu/wp-content/uploads/sites/67/2019/01/Iami_Kosuke-2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20190123T120000
DTEND;TZID=America/Los_Angeles:20190123T133000
DTSTAMP:20260430T105229
CREATED:20190118T194701Z
LAST-MODIFIED:20190122T214512Z
UID:1564-1548244800-1548250200@css.stat.ucla.edu
SUMMARY:Kosuke Imai\, Harvard University
DESCRIPTION:Title:  Matching Methods for Causal Inference with Time-Series Cross-Section Data\nAbstract:  Matching methods aim to improve the validity of causal inference in observational studies by reducing model dependence and offering intuitive diagnostics. While they have become a part of standard tool kit for empirical researchers across disciplines\, matching methods are rarely used when analyzing time-series cross-section (TSCS) data\, which consist of a relatively large number of repeated measurements on the same units. We develop a methodological framework that enables the application of matching methods to TSCS data. In the proposed approach\, we first match each treated observation with control observations from other units in the same time period that have an identical treatment history up to the pre-specified number of lags. We use standard matching and weighting methods to further refine this matched set so that the treated observation has outcome and covariate histories similar to those of its matched control observations. Assessing the quality of matches is done by examining covariate balance. After the refinement\, we estimate both short-term and long-term average treatment effects using the difference-in-differences estimator\, accounting for a time trend. We also show that the proposed matching estimator can be written as a weighted linear regression estimator with unit and time fixed effects\, providing model-based standard errors. We illustrate the proposed methodology by estimating the causal effects of democracy on economic growth\, as well as the impact of inter-state war on inheritance tax. The open-source software is available for implementing the proposed matching methods.\nCo-sponsored with the Political Science Department\, Statistics Department and the Center for Social Statistics\nMore on Prof. Imai
URL:https://css.stat.ucla.edu/event/kosuke-imai-harvard-university/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States
CATEGORIES:Ccpr Seminar,css seminar,Divisional Publish
ATTACH;FMTTYPE=image/png:https://css.stat.ucla.edu/wp-content/uploads/sites/67/2019/01/Iami_Kosuke-1.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20181017T120000
DTEND;TZID=America/Los_Angeles:20181017T133000
DTSTAMP:20260430T105229
CREATED:20181003T204102Z
LAST-MODIFIED:20181017T172052Z
UID:1525-1539777600-1539783000@css.stat.ucla.edu
SUMMARY:Erin Hartman\, University of California Los Angeles
DESCRIPTION:Title: Covariate Selection for Generalizing Experimental Results\nAbstract: Researchers are often interested in generalizing the average treatment effect (ATE) estimated in a randomized experiment to non-experimental target populations. Researchers can estimate the population ATE without bias if they adjust for a set of variables affecting both selection into the experiment and treatment heterogeneity. Although this separating set has simple mathematical representation\, it is often unclear how to select this set in applied contexts. In this paper\, we propose a data-driven method to estimate a separating set. Our approach has two advantages. First\, our algorithm relies only on the experimental data. As long as researchers can collect a rich set of covariates on experimental samples\, the proposed method can inform which variables they should adjust for. Second\, we can incorporate researcher-specific data constraints. When researchers know certain variables are unmeasurable in the target population\, our method can select a separating set subject to such constraints\, if one is feasible. We validate our proposed method using simulations\, including naturalistic simulations based on real-world data.\nCo-Sponsored with The Center for Social Statistics\nMore on Prof. Hartman
URL:https://css.stat.ucla.edu/event/erin-hartman-university-of-california-los-angeles/
LOCATION:CCPR Seminar Room\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States\, 101 Sumner Ave\, United States
CATEGORIES:Ccpr Seminar,css seminar,Divisional Publish
ORGANIZER;CN="CCPR%20Seminars":MAILTO:seminars@ccpr.ucla.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180313T140000
DTEND;TZID=America/Los_Angeles:20180313T151500
DTSTAMP:20260430T105229
CREATED:20180312T165958Z
LAST-MODIFIED:20180312T165958Z
UID:1486-1520949600-1520954100@css.stat.ucla.edu
SUMMARY:Jake Bowers\, University of Illinois at Urbana-Champaign
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nRules of Engagement in Evidence-Informed Policy: Practices and Norms of Statistical Science in Government\n\nCollaboration between statistical scientists (data scientists\, behavioral and social scientists\, statisticians) and policy makers promises to improve government and the lives of the public. And the data and design challenges arising from governments offer academics new chances to improve our understanding of both extant methods and behavioral and social science theory. However\, the practices that ensure the integrity of statistical work in the academy — such as transparent sharing of data and code — do not translate neatly or directly into work with governmental data and for policy ends. This paper proposes a set of practices and norms that academics and practitioners can agree on before launching a partnership so that science can advance and the public can be protected while policy can be improved. This work is at an early stage. The aim is a checklist or statement of principles or memo of understanding that can be a template for the wide variety of ways that statistical scientists collaborate with governmental actors. \n\nSpeaker:\nJake Bowers\, Associate Professor at University of Illinois and Fellow of the Office of Evaluation Sciences\nsite
URL:https://css.stat.ucla.edu/event/jake-bowers-university-of-illinois-at-urbana-champaign/
LOCATION:Franz Hall 2258A\, Franz Hall 2258A
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180221T120000
DTEND;TZID=America/Los_Angeles:20180221T133000
DTSTAMP:20260430T105229
CREATED:20180209T002136Z
LAST-MODIFIED:20180209T002136Z
UID:1413-1519214400-1519219800@css.stat.ucla.edu
SUMMARY:Yu Xie\, Princeton
DESCRIPTION:The California Center for Population Research and the Center for Social Statistics Presents:\nHeterogeneous Causal Effects: A Propensity Score Approach\nHeterogeneity is ubiquitous in social science.  Individuals differ not only in background characteristics\, but also in how they respond to a particular treatment. In this presentation\, Yu Xie argues that a useful approach to studying heterogeneous causal effects is through the use of the propensity score. He demonstrates the use of the propensity score approach in three scenarios: when ignorability is true\, when treatment is randomly assigned\, and when ignorability is not true but there are valid instrumental variables. \nSpeaker:\nYu Xie\, Professor\, Princeton\nsite
URL:https://css.stat.ucla.edu/event/yu-xie-princeton/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180206T140000
DTEND;TZID=America/Los_Angeles:20180206T153000
DTSTAMP:20260430T105229
CREATED:20180129T183306Z
LAST-MODIFIED:20180129T183306Z
UID:1368-1517925600-1517931000@css.stat.ucla.edu
SUMMARY:Per Block\, ETH Zurich (Swiss Federal Institute of Technology in Zurich)
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nModelling Mobility Tables as Weighted Networks\nContemporary research on occupational mobility\, i.e. how people move between jobs\, tends to view mobility as being mostly determined by individual and occupational characteristics. These studies focus on people’s sex\, ethnicity\, age\, education or class origin and how they get access to jobs of different wages\, working conditions\, desirability\, skill profiles and job security. Consequently\, observations in occupational mobility tables are understood as independent of one another\, which allows the use of a variety of well-developed statistical models. As opposed to these “classical” approaches focussed on individual and occupational characteristics\, I am interested in modelling and understanding endogenously emerging patterns in occupational mobility tables. These emergent patterns arise from the social embedding of occupational choices\, when occupational transitions of different individuals influence each other. To analyse these emergent patterns\, I conceptualise a disaggregated mobility table as a network in which occupations are the nodes and connections are made of individuals transitioning between occupations.\n\n\nIn this paper\, I present a statistical model to analyse these weighted mobility networks. The approach to modelling mobility as an interdependent system is inspired by the exponential random graph model (ERGM); however\, some differences arise from ties being weighted as well as from specific constraints of mobility tables. The model is applied to data on intra-generational mobility to analyse the interdependent transitions of men and women through the labour market\, as well as to understanding the extent to which clustering in mobility can be modelled by exogenously defined social classes or through endogenous structures.\n  \nPer Block\, ETH Zurich (Swiss Federal Institute of Technology in Zurich)\nsite
URL:https://css.stat.ucla.edu/event/per-block-eth-zurich/
LOCATION:Franz Hall 2258A\, Franz Hall 2258A
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20180124T120000
DTEND;TZID=America/Los_Angeles:20180124T133000
DTSTAMP:20260430T105229
CREATED:20180119T224032Z
LAST-MODIFIED:20180119T225154Z
UID:1361-1516795200-1516800600@css.stat.ucla.edu
SUMMARY:Rob Warren\, University of Minnesota
DESCRIPTION:The California Center for Population Research and the Center for Social Statistics presents:\nWhen Should Researchers Use Inferential Statistics When Analyzing Data on Full Populations?\nMany researchers uncritically use inferential statistical procedures (e.g.\, hypothesis tests) when analyzing complete population data—a situation in which inference may seem unnecessary. We begin by reviewing and analyzing the most common rationales for employing inferential procedures when analyzing full population data. Two common rationales—having to do with handling missing data and generalizing results to other times and/or places—either lack merit or amount to analyzing sample (not population) data.  Whether it is appropriate to use inferential procedures depends on whether researchers are analyzing sample or population data and on whether they seek to make causal or descriptive claims. When doing descriptive research\, the distinction between sample and population data is paramount: Inferential statistics should only be used to analyze sample data (to account for sampling variability) and never to analyze population data. When doing causal research\, the distinction between sample data and population data is unimportant: Inferential procedures can and should always be used to distinguish (for example) robust associations from those that may have come about by chance alone. Crucially\, using inferential procedures to analyze population data to make descriptive claims can lead to incorrect substantive conclusions—especially when population sizes and/or effect sizes are small. \nSpeaker:\nRob Warren\, Professor of Sociology and Director of the Minnesota Population Center\nsite
URL:https://css.stat.ucla.edu/event/rob-warren-university-minnesota/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171212T140000
DTEND;TZID=America/Los_Angeles:20171212T153000
DTSTAMP:20260430T105229
CREATED:20171211T190142Z
LAST-MODIFIED:20171211T190142Z
UID:1354-1513087200-1513092600@css.stat.ucla.edu
SUMMARY:Nathaniel Osgood\, University of Saskatchewan
DESCRIPTION:The UCLA Department of Community Health Sciences and the Center for Social Statistics presents:\nDynamic Modeling for Health in the Age of Big Data\nTraditional approaches to public health concerns have conferred great advances in the duration and quality of life. Public health interventions – from improved sanitation efforts\, to vaccination campaigns\, to contact tracing and environmental regulations – have helped reduce common risks to health throughout many areas of the world. Unfortunately\, while traditional methods from the health sciences have proven admirably suited for addressing traditional challenges\, a troubling crop of complex health challenges confront the nation and the world\, and threaten to stop – and even reverse the – rise in length and quality of life that many have taken for granted. Examples include multi-factorial problems such as obesity and obesity-related chronic disease\, the spread of drug-resistant and rapidly mutating pathogens that evade control efforts\, and “syndemics” of mutually reinforcing health conditions (such as Diabetes and TB; substance abuse\, violence and HIV/AIDS; obesity & stress). Such challenges have proven troublingly policy resistant\, with interventions being thwarted by “blowback” from the complex feedbacks involved\, and attendant costs threaten to overwhelm health care systems. In the face of such challenges public health decision makers are increasingly supplementing their toolbox using “system science” techniques. Such methods – also widely known as “complex systems approaches” – provide a way to understand a system’s behavior as a whole and as more than the sum of its parts\, and a means of anticipating and managing the behavior of a system in more judicious and proactive fashion. However\, such approaches offer substantially greater insight and power when combined with rich data sources. Within this talk\, we will highlight the great promise afforded by combining of Systems Science techniques and rich data sources\, particularly emphasizing the role of cross-linking models with “big data” offering high volume\, velocity\, variety and veracity. Examples of such data include fine-grained temporal and spatial information collected by smartphone-based and wearable as well as building and municipal sensors\, data from social media posts and search behavior\, helpline calls\, website accesses and rich cross-linked databases. Decision-oriented models grounded by such novel data sources can allow for articulated theory building regarding difficult-to-observe aspects of human behavior. Such models can also aid in informing evaluation of and judicious selection between sophisticated interventions to lessen the health burden of a wide variety of health conditions. Such models are particularly powerful when complemented by machine learning and computational statistics techniques that permit recurrent model regrounding in the newest evidence\, and which allow a model to knit together holistic portrait of the system as a whole\, and which support grounded investigation of between intervention strategies tradeoffs. \nSponsored by The Department of Community Health Sciences along with the Center for Social Statistics and the California Center for Population Research \nSpeaker:\nNathaniel Osgood\, Professor\, Department of Computer Science\, Associate Faculty at Department of Community Health & Epidemiology and Bioengineering Division at the University of Saskatchewan
URL:https://css.stat.ucla.edu/event/nathaniel-osgood-saskatchewan/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171108T153000
DTEND;TZID=America/Los_Angeles:20171108T163000
DTSTAMP:20260430T105229
CREATED:20171103T183129Z
LAST-MODIFIED:20171103T202044Z
UID:1334-1510155000-1510158600@css.stat.ucla.edu
SUMMARY:Hadley Wickham\, RStudio
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nProgramming data science with R & the tidyverse\nTidy evaluation is a new framework for non-standard evaluation that\nwill be used throughout tidyverse. In this talk\, I’ll introduce you to\nthe problem that tidy eval solves\, illustrated with examples of the\nvarious approaches used in R. I’ll then explain the most important\ncomponents so that you can start writing your own functions instead of\ncopying and pasting tidyr and dplyr code. I’ll finish with a small\nshiny app that shows how tidy eval is a natural fit for handling user\ninput. \nHadley Wickham\, RStudio\nhttp://hadley.nz/
URL:https://css.stat.ucla.edu/event/hadley-wickham-rstudio/
LOCATION:1200 Rolfe Hall\, 1200 Rolfe Hall
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171024T140000
DTEND;TZID=America/Los_Angeles:20171024T150000
DTSTAMP:20260430T105229
CREATED:20171016T182654Z
LAST-MODIFIED:20171016T182837Z
UID:1324-1508853600-1508857200@css.stat.ucla.edu
SUMMARY:Sander Greenland\, UCLA Department of Epidemiology
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nStatistical Significance and Discussion of the Challenges of Avoiding the Abuse of Statistical Methodology\nSander Greenland will offer his perspective on the paper\, “Redefine Statistical Significance”\, which was the topic of the previous week’s seminar. Also he will discuss the challenges of avoiding the abuse of statistical methodology. \nSpeaker:\nSander Greenland\, Professor Emeritus\, UCLA Department of Epidemiology
URL:https://css.stat.ucla.edu/event/sander-greenland-ucla/
LOCATION:1434A Physics and Astronomy\, 1434A Physics and Astronomy\, Los Angeles\, CA\, 90098\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171017T140000
DTEND;TZID=America/Los_Angeles:20171017T150000
DTSTAMP:20260430T105229
CREATED:20210424T023825Z
LAST-MODIFIED:20210424T023825Z
UID:2081-1508248800-1508252400@css.stat.ucla.edu
SUMMARY:Daniel Benjamin\, USC Dornsife Center for Economic and Social Research
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nRedefine Statistical Significance\nDaniel Benjamin will discuss his paper (written by him and 71 other authors)\, “Redefine Statistical Significance”. The paper proposes that the default p-value threshold should be changed from 0.05 to 0.005. \nThe paper is available at this link. \nSpeaker:\nDaniel Benjamin\, Associate Professor\, USC Dornsife Center for Economic and Social Research \n 
URL:https://css.stat.ucla.edu/event/daniel-benjamin-usc-dornsife-center-for-economic-and-social-research/
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20171017T140000
DTEND;TZID=America/Los_Angeles:20171017T150000
DTSTAMP:20260430T105229
CREATED:20171016T181816Z
LAST-MODIFIED:20171016T182927Z
UID:1315-1508248800-1508252400@css.stat.ucla.edu
SUMMARY:Daniel Benjamin\, USC Dornsife Center for Economic and Social Research
DESCRIPTION:The UCLA Department of Statistics and the Center for Social Statistics presents:\nRedefine Statistical Significance\nDaniel Benjamin will discuss his paper (written by him and 71 other authors)\, “Redefine Statistical Significance”. The paper proposes that the default p-value threshold should be changed from 0.05 to 0.005. \nThe paper is available at this link. \nSpeaker:\nDaniel Benjamin\, Associate Professor\, USC Dornsife Center for Economic and Social Research \n 
URL:https://css.stat.ucla.edu/event/daniel-benjamin-usc/
LOCATION:1434A Physics and Astronomy\, 1434A Physics and Astronomy\, Los Angeles\, CA\, 90098\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170609T120000
DTEND;TZID=America/Los_Angeles:20170609T130000
DTSTAMP:20260430T105229
CREATED:20170530T165922Z
LAST-MODIFIED:20170530T180714Z
UID:1262-1497009600-1497013200@css.stat.ucla.edu
SUMMARY:James Robins\, Harvard University
DESCRIPTION:The UCLA Departments of Epidemiology\, Biostatistics\, Statistics and the Center for Social Statistics presents:\nCausal Methods in Epidemiology: Where has it got us and what can we expect in the future?\nThe principal focus of Dr. Robins’ research has been the development of analytic methods appropriate for drawing causal inferences from complex observational and randomized studies with time-varying exposures or treatments. The new methods are to a large extent based on the estimation of the parameters of a new class of causal models – the structural nested models – using a new class of estimators – the G estimators.\n\nPlease RSVP: https://goo.gl/wScewQ \n\nSpeaker:\nJames Robins\, Mitchell L. and Robin LaFoley Dong Professor of Epidemiology\, Harvard University\nhttps://www.hsph.harvard.edu/james-robins/
URL:https://css.stat.ucla.edu/event/james-robins-harvard-university/
LOCATION:Room 33-105 CHS Building\, 650 Charles E Young Drive South\, Los Angeles\, CA\, 90095 \, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20170524T120000
DTEND;TZID=America/Los_Angeles:20170524T133000
DTSTAMP:20260430T105229
CREATED:20170323T201513Z
LAST-MODIFIED:20170501T205334Z
UID:784-1495627200-1495632600@css.stat.ucla.edu
SUMMARY:Shahryar Minhas\, Duke University
DESCRIPTION:The Center for Social Statistics Presents:\nPredicting the Evolution of Intrastate Conflict: Evidence from Nigeria\nThe endogenous nature of civil conflict has limited scholars’ abilities to draw clear inferences about the drivers of conflict evolution. We argue that three primary features characterize the complexity of intrastate conflict: (1) the interdependent relationships of conflict between actors; (2) the impact of armed groups on violence as they enter or exit the conflict network; and (3) the ability of civilians to influence the strategic interactions of armed groups. Using ACLED event data on Nigeria\, we apply a novel network-based approach to predict the evolution of intrastate conflict dynamics. Our network approach yields insights about the effects of civilian victimization and key actors entering the conflict. Attacks against civilians lead groups to both be more violent\, and to become the targets of attacks in subsequent periods. Boko Haram’s entrance into the civil war leads to an increase in violence even in unrelated dyads. Further\, our approach significantly outperforms more traditional dyad-group approaches at predicting the incidence of conflict.\n  \nSpeaker: \nShahryar Minha\, Postdoctoral Fellow\, Duke University\nAssistant Professor\, Michigan State University\nDepartment of Political Science and the Social Science Data Analytics Program (SSDA) \nhttp://s7minhas.com/
URL:https://css.stat.ucla.edu/event/shahryar-minhas/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;VALUE=DATE:20170423
DTEND;VALUE=DATE:20170426
DTSTAMP:20260430T105229
CREATED:20170327T184026Z
LAST-MODIFIED:20170501T204445Z
UID:928-1492905600-1493164799@css.stat.ucla.edu
SUMMARY:West Coast Experiments Conference\, UCLA 2017
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nWest Coast Experiments Conference\, UCLA 2017\nhttps://www.eventbrite.com/e/west-coast-experiments-2017-ucla-tickets-32968584925 \nThe tenth annual West Coast Experiments Conference will be held at UCLA on Monday\, April 24 and Tuesday\, April 25\, 2017\, preceded by in-depth methods training workshops on Sunday\, April 23. The WCE is an annual conference that brings together leading scholars and graduate students in economics\, political science and other social sciences who share an interest in causal identification broadly speaking. Now in its tenth year\, the WCE is a venue for methodological instruction and debate over design-based and observational methods for causal inference\, both theory and applications. \nThe speakers are Judea Pearl\, Rosa Matzkin\, Niall Cardin\, Angus Deaton\, Chris Auld\, Jeff Wooldridge\, Ed Leamer\, Karim Chalak\, Rodrigo Pinto\, Clark Glymour\, Elias Barenboim\, Adam Glynn\, and Karthika Mohan. \nRegistration is free\, but you must register at wce2017ucla.eventbrite.com to get a ticket for each day you plan to attend. Registration is first-come-first-served. We also will host free in-depth methods training workshops on the afternoon of Sunday\, April 23. We are currently planning these workshops so please watch this space for upcoming details. The topics will include causal graphs and big data. You can register for these workshops when you register for the conference. \nThis conference is funded by a generous grant from the Alfred P. Sloan Foundation and sponsored by the UCLA Department of Political Science\, the UCLA California Center for Population Research\, the UCLA Center for Social Statistics\, and the UCR School of Public Policy. The organizing committee this year is Chad Hazlett\, Judea Pearl\, Rodrigo Pinto\, and Manisha Shah. \nFor those unable to attend\, we will be creating a conference webpage to archive the papers and presentations. Please check back to the Eventbrite announcement page one week prior to the conference where we will post the URL for the paper and presentation archive. \nhttps://www.eventbrite.com/e/west-coast-experiments-2017-ucla-tickets-32968584925 \n 
URL:https://css.stat.ucla.edu/event/west-coast-experiments-conference-ucla-2017/
LOCATION:UCLA\, Los Angeles\, CA\, 90024\, United States
CATEGORIES:conference,css seminar,css workshop
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160527T120000
DTEND;TZID=America/Los_Angeles:20160527T140000
DTSTAMP:20260430T105229
CREATED:20170324T180812Z
LAST-MODIFIED:20170501T204235Z
UID:865-1464350400-1464357600@css.stat.ucla.edu
SUMMARY:Ilan H. Meyer & Mark S. Handcock\, UCLA
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nInnovative Sampling Approaches for Hard to Reach Populations: Design of a National Probability Study of Lesbians\, Gay Men\, Bisexuals\, and Transgender Peoples and  Network Sampling of Hard to Reach Populations\nSpeakers: \nIlan H. Meyer\, Williams Distinguished Senior Scholar for Public Policy at the Williams Institute \nMark S. Handcock\, Professor of Statistics at UCLA and Director of the Center for Social Statistics \nDescription: \nCome for the exciting seminar then stay for the free lunch and discussion. A seminar led by Ilan H. Meyer followed immediately by a Brown Bag Lunch led by Mark S. Handcock. \nDr. Meyer is Principal Investigator of the Generations and TransPop Surveys. Generations is a survey of a nationally representative sample of 3 generations of lesbians\, gay men\, and bisexuals. TransPop is the first national probability sample survey of transgender individuals in the United States. Both studies attempt to obtain large nationally representative samples of hard to reach populations. Dr. Meyer will review sampling issues with LGBT populations and speak on the importance of measuring population health of LGBTs and the underlying aspects in designing a national probability survey. \nFrom a contrasting perspective\, the field of Survey Methodology is facing many challenges. The general trend of declining response rates is making it harder for survey researchers to reach their intended population of interest using classical survey sampling methods. \nIn the followup Brown Bag Lunch\, led by Mark S. Handcock\, participants will discuss statistical challenges and approaches to sampling hard to reach populations. Transgenders\, for example\, are a rare and stigmatized population. If the transgender community exhibits networked social behavior\, then network sampling methods may be useful approaches that compliment classical survey methods.\nParticipants are encouraged to speak on ideas of statistical methods for surveys.
URL:https://css.stat.ucla.edu/event/ilan-h-meyer-mark-s-handcock-ucla/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160331T143000
DTEND;TZID=America/Los_Angeles:20160331T160000
DTSTAMP:20260430T105229
CREATED:20170501T204927Z
LAST-MODIFIED:20170501T204927Z
UID:1221-1459434600-1459440000@css.stat.ucla.edu
SUMMARY:Rick Dale\, University of California\, Merced
DESCRIPTION:The Center for Social Statistics Presents:\nQuantifying the dynamics of multimodal communication with multimodal data\nAbstract: Human communication is built upon an array of signals\, from body movement to word selection. The sciences of language and communication tend to study these signals individually. However\, natural human communication uses all these signals together simultaneously\, and in complex social systems of various sizes. It is an open puzzle to uncover how this multimodal communication is structured in time and organized at different scales. Such a puzzle includes analysis of two-person interactions. It also involves an understanding of much larger systems\, such as communication over social media at an unprecedentedly massive scale. \nCollaborators and I have explored communication across both of these scales\, and I will describe examples in the domain of conflict. For example\, we’ve studied conflict communication in two-person interactions using video analysis of body and voice dynamics. At the broader scale\, we have also used large-scale social media behavior (Twitter) during a massively shared experience of conflict\, the 2012 Presidential Debates. These projects reveal the importance of dynamics. In two-person conflict\, for example\, signal dynamics (e.g.\, body\, voice) during interaction can reveal the quality of that interaction. In addition\, collective behavior on Twitter can be predicted even by simple linear models using debate dynamics between Obama and Romney (e.g.\, one interrupting the other). \nThe collection\, quantification\, and modeling of multitemporal and multivariate datasets hold much promise for new kinds of interdisciplinary collaborations. I will end by discussing how they may guide new theoretical directions for pursuing the organization and temporal structure of multimodality in communication. \nUrl: http://statistics.ucla.edu/seminars/2016-03-31/2:30pm/314-royce-hall
URL:https://css.stat.ucla.edu/event/rick-dale-university-california-merced/
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20160329T143000
DTEND;TZID=America/Los_Angeles:20160329T153000
DTSTAMP:20260430T105229
CREATED:20170323T225738Z
LAST-MODIFIED:20170501T204151Z
UID:829-1459261800-1459265400@css.stat.ucla.edu
SUMMARY:Betsy Sinclair\, Washington University in St Louis
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nElectronic Homestyle: Tweeting Ideology\nAbstract: Ideal points are central to the study of political partisanship and an essential component to our understanding of legislative and electoral behavior. We employ automated text analysis on tweets from Members of Congress to estimate their ideal points using Naive Bayes classification and Support Vector Machine classification. We extend these tools to estimate the proportion of partisan speech used in each legislator’s tweets. We demonstrate an association between these measurements\, existing ideal point measurements\, and district ideology. \n 
URL:https://css.stat.ucla.edu/event/betsy-sinclair-washington-university-st-louis/
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151113T120000
DTEND;TZID=America/Los_Angeles:20151113T133000
DTSTAMP:20260430T105229
CREATED:20170324T180355Z
LAST-MODIFIED:20170501T204132Z
UID:861-1447416000-1447421400@css.stat.ucla.edu
SUMMARY:Reproducibility of Statistical Results
DESCRIPTION:The Center for Social Statistics Presents:\n\n\nMark S. Handcock \n(Professor\, Statistics) \nJeffrey B. Lewis \n(Professor\, Political Science) \nMarc A. Suchard \n(Professor\, Biomathematics\, Biostatistics and Human Genetics) \nAbstract: \nReproducibility is one of the main principles of the scientific method. This panel of scholars will discuss issues in the importance of replication of statistical results. Increasing attention is being paid to improve reporting and hence reproducibility in the social and medical sciences. This panel will discuss some key concerns in study replication\, initiatives for increasing replication\, and then open the floor to discussion of how we move forward as a scientific community.
URL:https://css.stat.ucla.edu/event/reproducibility-statistical-results/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20151015T120000
DTEND;TZID=America/Los_Angeles:20151015T133000
DTSTAMP:20260430T105229
CREATED:20170323T224804Z
LAST-MODIFIED:20170501T204012Z
UID:816-1444910400-1444915800@css.stat.ucla.edu
SUMMARY:Aude Hofleitner\, Facebook
DESCRIPTION:The Center for Social Statistics Presents:\nInferring and understanding travel and migration movements at a global scale\nAbstract: Despite extensive work on the dynamics and outcomes of large-scale migrations\, timely and accurate estimates of population movements do not exist. While censuses\, surveys\, and observational data have been used to measure migration\, estimates based on these data sources are constrained in their inability to detect unfolding migrations\, and lack temporal and demographic detail. In this study\, we present a novel approach for generating estimates of migration that can measure movements of particular demographic groups across country lines. \nSpecifically\, we model migration as a function of long-term moves across countries using aggregated Facebook data. We demonstrate that this methodological approach can be used to produce accurate measures of past and ongoing migrations – both short-term patterns and long-term changes in residence. Several case studies confirm the validity of our approach\, and highlight the tremendous potential of information obtained from online platforms to enable novel research on human migration events. \nIf you are interested in meeting with or joining the speaker for lunch\, please send email to Seminars@ccpr.ucla.edu
URL:https://css.stat.ucla.edu/event/aude-hofleitner-facebook/
LOCATION:4240 Public Affairs Building\, 4240 Public Affairs Building\, Los Angeles\, CA\, 90095\, United States
CATEGORIES:css seminar
END:VEVENT
END:VCALENDAR