gov3009-l March 2010

gov3009-l@lists.fas.harvard.edu

1 participants
4 discussions

by Matt Blackwell

Hi all, Just a note to let you know that we will not be having an Applied Statistics workshop this Wednesday, March 31st. We will return next Wednesday, April 7th with Thomas Kane from the Graduate School of Education. Cheers, matt.

14 years

Blitzstein on "Stability and Estimability of Centrality in Networks"

by Matt Blackwell

Hello, We hope you will join us this Wednesday, March 24th at the Applied Statistics workshop when we will be happy to have Joe Blitzstein (Department of Statistics). Details and an abstract are below. A light lunch will be served. Thanks! "Stability and Estimability of Centrality in Networks" Joe Blitzstein Harvard Department of Statistics March 24th, 2010, 12 noon K354 CGIS Knafel (1737 Cambridge St) Abstract: "Centrality" is one of the most widely-used notions network analysis, attempting to measure the importance of a node. Yet despite being such a central idea to the study of networks, there is no standard definition; rather, there are many incompatible definitions, e.g., based on degrees, shortest paths, and eigenvectors of the adjacency matrix. We will attempt to sort through this menagerie of definitions of centrality from the perspective of stability and estimability. That is, if the network is perturbed slightly or is measured with noise, how much does centrality change? If -- as is usually the case -- only a sample of the full network is observed, which of these notions can be reliably estimated from the sample? Cheers, matt. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University email: mblackwell(a)iq.harvard.edu url: http://people.fas.harvard.edu/~blackwel/

14 years, 1 month

Zajonc on "Bayesian Inference for Dynamic Treatment Regimes"

by Matt Blackwell

Hello, We hope you will join us this Wednesday, March 10th at the Applied Statistics workshop when we will be happy to have Tristan Zajonc (Harvard Kennedy School). Details and an abstract are below. A light lunch will be served. Thanks! "Bayesian Inference for Dynamic Treatment Regimes" Tristan Zajonc Harvard Kennedy School March 10th, 2010, 12 noon K354 CGIS Knafel (1737 Cambridge St) Abstract: Policies in health, education, and economics often unfold sequentially and adapt to developing conditions. Doctors treat patients over time depending on their prognosis, educators assign students to courses given their past performance, and governments design social insurance programs to address dynamic needs and incentives. I present the Bayesian perspective on causal inference and optimal treatment choice for these types of adaptive policies or dynamic treatment regimes. The key empirical difficulty is dynamic selection into treatment: intermediate outcomes are simultaneously pre-treatment confounders and post-treatment outcomes, causing standard program evaluation methods to fail. Once properly formulated, however, sequential selection into treatment on past observables poses no unique difficulty for model-based inference, and analysis proceeds equivalently to a full-information analysis under complete randomization. I consider optimal treatment choice as a Bayesian decision problem. Given data on past treated and untreated units, analysts propose treatment rules for future units to maximize a policymaker's objective function. When policymaker’s have multidimensional preferences, the approach can estimate the set of feasible outcomes or the tradeoff between equity and efficiency. I demonstrate these methods through an application to optimal student tracking in ninth and tenth grade mathematics. An easy to implement optimal dynamic tracking regime increases tenth grade mathematics achievement 0.1 standard deviations above the status quo, with no corresponding increase in inequality. The proposed methods provide a flexible and principled approach to causal inference for sequential treatments and optimal treatment choice under uncertainty. Cheers, matt. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University email: mblackwell(a)iq.harvard.edu url: http://people.fas.harvard.edu/~blackwel/

14 years, 1 month

Steenburgh on "Substitution Patterns of the Random Coefficients Logit"

by Matt Blackwell

Hello, We hope you will join us this Wednesday, March 3rd at the Applied Statistics workshop when we will be happy to have Thomas Steenburgh (Harvard Business School). Details, an abstract, and a link to the paper are below. A light lunch will be served. Thanks! "Substitution Patterns of the Random Coefficients Logit" Thomas Steenburgh Harvard Business School March 3rd, 2010, 12 noon K354 CGIS Knafel (1737 Cambridge St) http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1535329 Abstract: Previous research suggests that the random coefficients logit is a highly flexible model that overcomes the problems of the homogeneous logit by allowing for differences in tastes across individuals. The purpose of this paper is to show that this is not true. We prove that the random coefficients logit imposes restrictions on individual choice behavior that limit the types of substitution patterns that can be found through empirical analysis, and we raise fundamental questions about when the model can be used to recover individuals’ preferences from their observed choices. Part of the misunderstanding about the random coefficients logit can be attributed to the lack of cross-level inference in previous research. To overcome this deficiency, we design several Monte Carlo experiments to show what the model predicts at both the individual and the population levels. These experiments show that the random coefficients logit leads a researcher to very different conclusions about individuals’ tastes depending on how alternatives are presented in the choice set. In turn, these biased parameter estimates affect counterfactual predictions. In one experiment, the market share predictions for a given alternative in a given choice set range between 17% and 83% depending on how the alternatives are displayed both in the data used for estimation and in the counterfactual scenario under consideration. This occurs even though the market shares observed in the data are always about 50% regardless of the display. Cheers, matt. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University email: mblackwell(a)iq.harvard.edu url: http://people.fas.harvard.edu/~blackwel/

14 years, 1 month

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gov3009-l March 2010