gov3009-l November 2012

gov3009-l@lists.fas.harvard.edu

1 participants
3 discussions

Applied Statistics Workshop: Hainmueller and Yamamoto on Wed., Nov. 28

by Konstantin Kashin

Hello all, We hope you can join us this Wednesday, November 28, 2012 for the final Applied Statistics Workshop of the semester. Jens Hainmueller (Associate Professor) and Teppei Yamamoto (Assistant Professor) from the Department of Political Science at MIT will give a presentation entitled "Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments". A light lunch will be served at 12 pm and the talk will begin at 12.15. Abstract: > For decades, market researchers have used conjoint analysis to understand > how consumers make decisions when faced with multi-dimensional choices. In > such analyses, respondents are asked to score or rank a set of > alternatives, where each alternative is defined by multiple attributes > which are varied randomly or intentionally. Political scientists are > frequently interested in parallel questions about decision-making, yet to > date conjoint analysis has seen little use within the field. In this > manuscript, we demonstrate the potential value of conjoint analysis in > political science, using examples about vote choice and immigrant admission > to the United States. In doing so, we develop a set of statistical tools > for drawing causal conclusions from stated preference data based on the > potential outcomes framework of causal inference. We discuss the causal > estimands of interest and provide a formal analysis of the assumptions > required for identifying those quantities. Prior conjoint analyses have > typically used designs which limit the number of unique conjoint profiles. > We employ a survey experiment to compare this approach to a fully > randomized approach. Both our formal analysis of the causal estimands and > our empirical results highlight the potential biases of common approaches > to conjoint analysis which restrict the number of profiles. An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. Best, Konstantin -- Konstantin Kashin Ph.D. Candidate in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>

11 years, 5 months

Applied Statistics Workshop: Cassandra Wolos Pattanayak on Wed., Nov. 14

by Konstantin Kashin

Hi all, We hope you can join us this Wednesday, November 14, 2012 for the Applied Statistics Workshop. Cassandra Wolos Pattanayak<http://www.people.fas.harvard.edu/~wolos/Site/Home.html>, a College Fellow from the Department of Statistics at Harvard University, will give a presentation entitled "A Potential Outcomes, and Typically More Powerful, Alternative to 'Cochran-Mantel-Haenszel'". A light lunch will be served at 12 pm and the talk will begin at 12.15. Abstract: > In studies of public health, outcome measures such as the odds ratio, rate > ratio, or efficacy are often estimated across strata to assess the overall > effect of active treatment versus control treatment. Patients may be > partitioned into such strata or blocks by experimental design, or, in > non-randomized studies, patients may be partitioned into subclasses based > on key covariates or estimated propensity scores to improve observed > covariate balance across treatment groups. In finite samples, there exist > tests and intervals for these estimands that can be more powerful than > tests and intervals created with Cochran-Mantel-Haenszel or analogous > procedures . The proposed methods multiply impute missing potential > outcomes within the Rubin Causal Model so that estimands can be directly > estimated. The assumptions underlying these typically more powerful methods > are appropriate in many circumstances, especially when the strata are based > on covariates highly predictive of treatment decisions and outcomes. When > used to draw inferences about a population from which the patients in the > study are considered a random sample, and the sample is large, these > methods are extremely similar to the classical methods. The proposed > approach is particularly relevant when assessing the safety of a new > treatment relative to a standard one because, under typical conditions, the > tests are more powerful and the intervals are shorter, thereby detecting > smaller differences. An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. Best, Konstantin -- Konstantin Kashin Ph.D. Candidate in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>

11 years, 5 months

Applied Statistics Workshop: Jon Bischof on Wed., Nov. 7

by Konstantin Kashin

Dear all, We hope you can join us this Wednesday, November 7, 2012 for the Applied Statistics Workshop from 12-1.30 pm. Jon Bischof, a Ph.D. candidate from the Department of Statistics at Harvard University, will give a presentation entitled "Summarizing Topical Content in Document Collections with Word Frequency and Exclusivity". A light lunch will be served at 12 pm and the talk will begin at 12.15. *Abstract: * > An ongoing challenge in the analysis of document collections is how to > summarize content in terms of a set of inferred themes that can be > interpreted substantively in terms of topics. However, the current practice > of summarizing themes in terms of most frequent words limits > interpretability by ignoring the differential use of words across topics. > We argue that words that are both frequent and exclusive to a theme are > more effective at characterizing topical content. We consider a setting > where professional editors have annotated documents to a collection of > topic categories, organized into a tree, in which leaf-nodes correspond to > the most specific topics. Each document is annotated to multiple > categories, at different levels of the tree. We introduce Hierarchical > Poisson Convolution (HPC) as a model to analyze annotated documents in this > setting. The model leverages the structure among categories defined by > professional editors to infer a clear semantic description for each topic > in terms of words that are both frequent and exclusive. We develop a > parallelized Hamiltonian Monte Carlo sampler that allows the inference to > scale to millions of documents. An up-to-date schedule for the workshop is available at http://www.iq.harvard.edu/events/node/1208. Best, Konstantin -- Konstantin Kashin Ph.D. Candidate in Government Harvard University Mobile: 978-844-0538 E-mail: kkashin(a)fas.harvard.edu Site: http://www.konstantinkashin.com/<http://people.fas.harvard.edu/%7Ekkashin/>

11 years, 5 months

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gov3009-l November 2012