gov3009-l September 2010

gov3009-l@lists.fas.harvard.edu

1 participants
4 discussions

Robins on "Pure (natural) direct and indirect effects"

by Matt Blackwell

Hi all, We hope you can join us for the Applied Statistics Workshop this Wednesday, September 29th, when we are excited to have Jamie Robins speaking on direct and indirect effects, based on joint work with Thomas Richardson. Details of his talk, an abstract, and a link to the paper are below. The workshop begins at 12 noon and we will serve a light lunch. Hope to see you there! "Pure (natural) direct and indirect effects, alternative counterfactual models, and the philosophy of science" James Robins Harvard School of Public Health September 29th, 2010, 12 noon K354 CGIS Knafel (1737 Cambridge St) Paper: http://www.iq.harvard.edu/events/sites/iq.harvard.edu.events/files/wp100.pdf Abstract: We consider four classes of graphical causal models: the Finest Fully Randomized Causally Interpretable Structured Tree Graph (FFRCISTG) of Robins (1986), the agnostic causal model of Spirtes et al. (1993), the Non-Parametric Structural Equation Model (NPSEM) of Pearl (2000), and the Minimal Counterfactual Model (MCM) which we introduce. The latter is referred to as ‘minimal’ because it imposes the minimal counterfactual independence assumptions required to identify those causal contrasts representing the effect of an ideal intervention on any subset of the variables in the graph. The causal contrasts identified by an MCM are, in general, a strict subset of those identified by a NPSEM associated with the same graph. We analyze various measures of the ‘direct’ causal effect, focusing on the pure direct effect (PDE), also called the ‘natural direct effect’. We show the PDE is a parameter that may be identified in a DAG viewed as a NPSEM, but not as an MCM. In spite of this, Pearl has given a scenario in which the PDE corresponds to the intent-to-treat parameter of a randomized experiment. We resolve this apparent paradox by showing that implicit within Pearl’s account is an extended causal DAG with additional variables in which there is a causal contrast that equals the pure direct effect of Pearl’s original NPSEM. Further, this contrast is identified from observational data on the original variables. Finally we relate our results to the work of Avin et al. (2005) on path-specific causal effects. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University url: http://people.fas.harvard.edu/~blackwel/

13 years, 7 months

Hidalgo on "The Structure of Economic Complexity"

by Matt Blackwell

Hi all, We hope you can join us for the Applied Statistics Workshop of the year this Wednesday, September 22nd, when we will be happy to have César A. Hidalgo presenting a paper entitled "The Structure of Economic Complexity." César is an Assistant Professor at the MIT Media Lab, where he leads the Macro Connections group, and a faculty associate at Harvard University’s Center for International Development Details of his talk and an abstract are below. The workshop begins at 12 noon and we will serve a light lunch. Hope to see you there! "The Structure of Economic Complexity" César A. Hidalgo Assistant Professor, Massachusetts Institute of Technology (MIT) September 22nd, 2010, 12 noon K354 CGIS Knafel (1737 Cambridge St) Abstract: With billions or products, billions of people, and trillions of interactions, the world economy is one of the most outstanding complex systems to have ever emerged. Can we use complexity science to improve our understanding of a system of such paramount complexity? In this talk I summarize recent research that uses networks to describe, characterize and understand differences in the productive structure of nations and their evolution. First, I show how the complexity of an economy can be quantified by looking at the structure of the network connecting countries to the products they export and that countries tend to approach a level of income which is dictated by the complexity of their economies. Then, I show how development is constrained by a projection of this network into the space of products, or Product Space, by demonstrating empirically that the evolution of countries comparative advantage is constrained by the structure of this network. To conclude I present a simple model that can account for some of the stylized facts that we uncover from our analysis and discuss how strategy and policy can be reinterpreted from this alternative perspective. Cheers, matt. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University url: http://people.fas.harvard.edu/~blackwel/

13 years, 7 months

Blackwell on "Multiple Overimputation: A Unified Approach to Measurement Error and Missing Data"

by Matt Blackwell

Hi all, We hope you can join us for the Applied Statistics Workshop this Wednesday, September 15th, when I will have the dubious honor of hosting myself, Matthew Blackwell, and presenting joint work with James Honaker and Gary King. Details of the talk and an abstract are below. The workshop begins at 12 noon and we will serve a light lunch. Hope to see you there! "Multiple Overimputation: A Unified Approach to Measurement Error and Missing Data" Matthew Blackwell (with James Honaker and Gary King) Governemnt September 15th, 2010, 12 noon K354 CGIS Knafel (1737 Cambridge St) Abstract: Social scientists typically devote considerable effort to reducing measurement error during data collection only to ignore the issue during data analysis. Although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative that generalizes the popular multiple imputation (MI) framework so that it treats missing data problems as a special case of extreme measurement error and corrects for both. Like MI, the proposed "multiple overimputation" (MO) framework is a simple two-step procedure. First, multiple (~5) completed copies of the dataset are created where cells measured without error are held constant, those missing are imputed from the distribution of predicted values, and cells (or entire variables) with measurement error are "overimputed," that is imputed from a predictive distribution with observation-level priors defined by the mismeasured values and available external information, if any. In the second step, analysts can then run whatever statistical method they would have run on each of the overimputed datasets as if there had been no missingness or measurement error; the results are then combined via a simple procedure. We also offer open source software that implements all the methods described herein. You can find a copy of the paper here: http://gking.harvard.edu/files/abs/measure-abs.shtml Cheers, matt. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University url: http://people.fas.harvard.edu/~blackwel/

13 years, 7 months

Fransden on "Nonparametric randomization inference on quantiles under imperfect compliance"

by Matt Blackwell

Hi all, We hope you can join us for the first Applied Statistics Workshop of the year this Wednesday, September 8th, when we will be happy to have Brigham Fransden who is a Robert Wood Johnson Scholar in Health Policy. Brigham received his PhD from the Department of Economics at MIT this past June. Details of his talk and an abstract are below. The workshop begins at 12 noon and we will serve a light lunch. Hope to see you there! "Nonparametric randomization inference on quantiles under imperfect compliance" Brigham Fransden Robert Wood Johnson Scholar in Health Policy September 8th, 2010, 12 noon K354 CGIS Knafel (1737 Cambridge St) Abstract: I develop a nonparametric approach to randomization inference on the effects of a treatment under imperfect compliance to a randomized assignment. I show how to construct exact finite-sample confidence intervals for the distribution of potential outcomes for the subgroup of subjects---compliers---whose treatment status is manipulated by the random assignment. An advantage of this approach is that it allows the effect of treatment to be heterogeneous and makes inference on the effects of the treatment across the distribution without imposing a parametric model. Cheers, matt. ~~~~~~~~~~~ Matthew Blackwell PhD Candidate Institute for Quantitative Social Science Department of Government Harvard University url: http://people.fas.harvard.edu/~blackwel/

13 years, 7 months

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gov3009-l September 2010