# Social Science Statistics Blog

November 2012
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### Recent Entries

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- App Stats: Bischof on "Summarizing Topical Content in Document Collections with Word Frequency and Exclusivity"

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26 November 2012

### App Stats: Hainmueller and Yamamoto on "Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments"

We hope you can join us this Wednesday, November 28, 2012 for the Applied Statistics Workshop. Jens Hainmueller and Teppei Yamamoto, Associate Professor and Assistant Professor, respectively, 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.

"Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices via Stated Preference Experiments"
Jens Hainmueller and Teppei Yamamoto
Department of Political Science, MIT
CGIS K354 (1737 Cambridge St.)
Wednesday, November 28th, 2012 12.00 pm

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.

Posted by Konstantin Kashin at 2:24 AM

11 November 2012

### App Stats: Pattanayak on "A Potential Outcomes, and Typically More Powerful, Alternative to 'Cochran-Mantel-Haenszel'"

We hope you can join us this Wednesday, November 14, 2012 for the Applied Statistics Workshop. Cassandra Wolos Pattanayak, 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.

"A Potential Outcomes, and Typically More Powerful, Alternative to 'Cochran-Mantel-Haenszel'"
Cassandra Wolos Pattanayak
Statistics Department, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, November 14th, 2012 12.00 pm

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.

Posted by Konstantin Kashin at 9:53 PM

5 November 2012

### App Stats: Bischof on "Summarizing Topical Content in Document Collections with Word Frequency and Exclusivity"

We hope you can join us this Wednesday, November 7, 2012 for the Applied Statistics Workshop. 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.

"Summarizing Topical Content in Document Collections with Word Frequency and Exclusivity"
Jon Bischof
Department of Statistics, Harvard University
CGIS K354 (1737 Cambridge St.)
Wednesday, November 7th, 2012 12.00 pm

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.

Posted by Konstantin Kashin at 11:29 AM