Presentations

    Nathan Kallus (MIT) - Regression-Robust Designs of Controlled Experiments Wednesday, April 30, 2014

    Abstract: Achieving balance between experimental groups is a cornerstone of causal inference. Without balance any observed difference may be attributed to a difference other than the treatment alone. In controlled/clinical trials, where the experimenter controls the administration of treatment, complete randomization of subjects has been the golden standard for achieving this balance because it allows for unbiased and consistent estimation and inference in the absence of any a priori knowledge or measurements. However, since estimator variance under complete...

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    Nick Beauchamp (Northeastern University) - Predicting, Extrapolating and Interpolating State-level Polls using Twitter Wednesday, April 23, 2014

    Abstract: Presidential, gubernatorial, and senatorial elections all require state-level polling, but even during presidential campaigns, state-level surveys remain sparse, erratically timed, and entirely neglected in uncompetitive states. Partly in response to these unmet needs in political and other domains, there have been numerous efforts to approximate various survey measures using social media data, but most of these approaches remain distinctly flawed, both methodologically and due to insufficient training data.  To remedy these flaws, this paper...

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    Finale Doshi (Harvard) - Prediction and Interpretation with Latent Variable Models Wednesday, April 16, 2014

    Abstract: Latent variable models provide a powerful tool for summarizing data through a set of hidden variables.  These models are generally trained to maximize prediction accuracy, and modern latent variable models now do an excellent job of finding compact summaries of the data with high predictive power.  However, there are many situations in which good predictions alone are not  sufficient. Whether the hidden variables have inherent value by providing insights  about the data, or whether we simply wish to improve a system, understanding...

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    Eleanor Neff Powell (Yale) - Money in Exile: Campaign Contributions and Committee Access Wednesday, April 9, 2014:
    money_in_exile.pdf

     Abstract: Corporations and political action committees (PACs) flood congressional elections with money. Understanding why they contribute is essential for determining how money in- fluences policy in Congress. To test theories of contributors’ motivations we exploit committee exile—the involuntary removal of committee members after a party loses a sizable number of seats, and the losses are unevenly distributed across committees. We use exile to show that business...

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    Krista Gile (University of Massachusetts Amherst) - New methods for inference from Respondent-Driven Sampling Data Wednesday, April 2, 2014

    Abstract: Respondent-Driven Sampling is type of link-tracing network sampling used to study hard-to-reach populations.  Beginning with a convenience sample, each person sampled is given 2-3 uniquely identified coupons to distribute to other members of the target population, making them eligible for enrollment in the study.  This is effective at collecting large diverse samples from my populations.

    Current estimation relies on sampling weights estimated by treating the sampling process as a random walk on the underlying network of social...

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    Herman van Dijk (Vrije Universiteit) - Forecasting with Many Models in Finance and Economics using Large Data Sets and Parallel Computing Wednesday, March 26, 2014:
    parallel_sequential_monte_carlo.pdf
    time-varying_combinations.pdf

    Abstract: We propose a Bayesian combination approach for multivariate predictive densities which relies upon a distributional state space representation of the combination weights. Several specifications of multivariate time-varying weights are introduced with a particular focus on weight dynamics driven by the past performance of the predictive densities and the use of learning mechanisms. In the proposed approach the model set can be incomplete, meaning that all models can be individually misspecified. A Sequential Monte Carlo method is proposed to...

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    Daniel O'Brien (Northeastern University) - Ecometrics in the Age of Big Data: Measuring and Assessing Neighborhood Characteristics Using Administrative Records Wednesday, March 12, 2014

    Presenter: Daniel O'Brien

    Abstract: The collection of large-scale administrative records in electronic form by many cities provides a new opportunity for the measurement and longitudinal tracking of neighborhood characteristics, but one that will require novel methodologies that convert such data into research-relevant measures. The current paper illustrates these challenges by developing measures of physical disorder from Boston’s “Constituent Relationship Management” (CRM) system. A sixteen-month archive of...

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    Ned Hall (Harvard) - In Praise of Causal Mechanisms Wednesday, March 5, 2014

    Abstract: Consider two theses about causation: (1) Causes are connected to their effects by way of mediating causal mechanisms or processes. (2) Scientific inquiry aims (at least in part) at discerning and describing the causal structure of our world. Some of the best contemporary work on causation claims—often implicitly, but sometimes quite explicitly—that, in giving an account of causation, we should sacrifice (1) for the sake of producing an account that makes the best sense of (2). I will first try to...

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    Adam Glynn (Emory Universiity) - Front-Door Difference-in-Differences Estimators: The Effects of Early In-person Voting on Turnout (joint work with Konstantin Kashin) Wednesday, February 26, 2014:
    front-door.pdf

    Presenter: Adam Glynn

    Abstract: In this paper, we develop front-door difference-in-differences estimators that utilize information from post-treatment variables in addition to information from pre-treatment covariates. Even when the front-door criterion does not hold, these estimators allow the identification of causal effects by utilizing assumptions that are analogous to standard difference-in-differences assumptions. We also demonstrate that causal effects can be bounded by front-door and front-door difference-in-differences estimators...

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    James Honaker (Harvard) - Sorting Algorithms for Qualitative Data to Recover Latent Dimensions with Crowdsourced Judgments Wednesday, February 12, 2014:
    sorting_algorithms.pdf

    Abstract: The Quicksort and Bubble Sort algorithms are commonly implemented procedures in computer science for sorting a set of numbers from low to high in an efficient number of processes using only pairwise comparisons. Because of such algorithms’ reliance on pairwise comparison, they lend themselves to any implementation where a simple judgment requires selecting a winner. We show how such algorithms, adapted for stochastic measurements, are an efficient way to harness human ”crowdsourced” coders who are...

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    Tyson Belanger (Harvard) - Fear, Hope, and War: Positive Inducements Help Win Wars Wednesday, February 5, 2014:
    fear_hope_and_war.pdf
    fhw_tables_-tyson_belanger.pdf

    Abstract:  How do states win wars against other states? We have three explanations. By selection effects, states choose more winnable wars. By warfighting, states use negative inducements so enemies fear fighting. And by peacemaking, states use positive inducements so enemies hope for settling. This article investigates peacemaking. It theorizes that states optimally produce war influence only if they efficiently combine both warfighting negative and peacemaking positive inducements. It measures positive inducements by law of war compliance, where...

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    Patrick Lam (Harvard) - Voter Persuasion in Compulsory Electorates: Evidence from a Field Experiment in Australia Wednesday, January 29, 2014:
    voter_persuasion.pdf

    Abstract: Most of the literature on grassroots campaigning focuses on mobilizing potential supporters to turn out to vote. The actual ability of partisan campaigns to boost support by changing voter preferences is unclear. We present the results of a field experiment the Australian Council of Trade Unions (ACTU) ran during the 2013 Australian Federal Election. The experiments were designed to minimize the conservative (the Coalition) vote as part of one of the largest and most extensively documented voter persuasion...

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    Not in My Backyard: Understanding Local Opposition to Undocumented Immigration Using a National Survey Experiment- Author: Jason Anastasopoulos Wednesday, December 4, 2013
    Author: Jason Anastasopoulos

    Abstract: Understanding what motivates animus toward undocumented immigrants presents several empirical challenges.  Estimates of undocumented immigrants at the state and metropolitan area level are unreliable, rendering studies which base conclusions on them questionable at best. Furthermore, because undocumented immigrants mostly come from Latin American countries, assessing the role that undocumented immigrant characteristics such as race or ethnic background...
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    Consumer Demand and Welfare Estimation in a Heterogeneous Population- Presenter: Stefan Hoderlein Wednesday, November 20, 2013

    Presenter: Stefan Hoderlein 

    Abstract: This is an overview about own recent econometric work related to the modeling of heterogeneity in applied consumer demand models. The focus will be on non-parametric random coefficient models. The main application will come from estimating gasoline demand; in particular, estimating the distribution of welfare effects of a 5% gasoline price change.

    ...
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