2015

12/2/2015- Edo Airoldi (Harvard)- Estimating causal effects in the presence of interfering units Wednesday, December 2, 2015

Title: Estimating causal effects in the presence of interfering units

Abstract: Classical approaches to causal inference largely rely on the assumption of “lack of interference”, according to which the outcome of an individual does not depend on the treatment assigned to others. In many applications, however, such as designing and evaluating the effectiveness of healthcare interventions that leverage social structure, assuming lack of interference is untenable. In fact, the effect of interference itself is often an inferential target of...

11/18/2015- Luke Miratrix (Harvard)- Estimating and assessing treatment effect variation in large-scale randomized trials with randomization inference Wednesday, November 18, 2015

Authors: Peng Deng, Avi Feller, Luke Miratrix

Abstract: Recent literature has underscored the critical role of treatment effect variation in estimating and understanding causal effects. This approach, however, is in contrast to much of the foundational research on causal inference; Neyman, for example, avoided such variation through his focus on the average treatment effect (ATE) and his definition of the confidence interval. We extend the Neymanian framework to explicitly allow both for treatment effect variation explained by covariates, known...

11/11/2015- Manuel Gomez Rodriguez (Harvard)- COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution Wednesday, November 11, 2015

Title: COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution

Abstract: Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes, information diffusion and network evolution, have been predominantly studied separately,...

11/4/2015- Albert-László Barabási (Northeastern)- Network Science: From structure to control Wednesday, November 4, 2015

Title: Network Science: From structure to control

Abstract: Systems as diverse as the world wide web, Internet or the cell are described by highly interconnected networks with amazingly complex topology. Recent studies indicate that these networks are the result of self-organizing processes governed by simple but generic laws, resulting in architectural features that makes them much more similar to each other than one would have expected by chance. I will discuss the order characterizing our interconnected world and its implications...

10/28/2015- Judith Lok (Harvard) - Defining and estimating causal direct and indirect effects when setting the mediator to specific values is not feasible Wednesday, October 28, 2015

Title: Defining and estimating causal direct and indirect effects when setting the mediator to specific values is not feasible

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10/21/2015- Peng Ding (Harvard)- Sensitivity Analysis Without Assumptions Wednesday, October 21, 2015

Title: Sensitivity Analysis Without Assumptions

Abstract: Unmeasured confounding may undermine the validity of causal inference with observational studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by assessing the potential influence of unmeasured confounding on the causal conclusions. However, previous sensitivity analysis approaches often make strong and untestable assumptions such as having a confounder that is binary, or having no interaction between the effects of the exposure and the confounder on the...

10/14/2015- Cynthia Rudin (MIT)- A Machine Learning Perspective on Causal Inference Wednesday, October 14, 2015

Title: A Machine Learning Perspective on Causal Inference

Abstract: Usually the terms "causal inference" and "machine learning" mix like oil and water. Machine learning models are often black box complicated functions that provide predictions without causal explanations. For causal inference, this kind of model is unacceptable. Maybe we can find ways to harness the predictive power of machine learning methods for the purpose of causal inference. I will discuss three very recent preliminary ideas, from the perspective of...

10/7/2015- Marc Ratkovic (Princeton) & Dustin Tingley (Harvard)- Sparse Estimation and Uncertainty with Application to Subgroup Analysis Wednesday, October 7, 2015

Title: Sparse Estimation and Uncertainty with Application to Subgroup Analysis

Abstract: We introduce a Bayesian method, LASSOplus, that unifies recent contributions in the sparse modeling literatures, while substantially extending upon pre-existing estimators in terms of both performance and flexibility. Unlike existing Bayesian variable selection methods, LASSOplus both selects and estimates effects, while returning estimated confidence intervals among discovered effects. Furthermore, we show how LASSOplus easily...

9/30/2015- James Greiner (Harvard)- Two Proposed Field RCTs in the Law Wednesday, September 30, 2015

Title: Two Proposed Field RCTs in the Law

Abstract: This talk will consist of a presentation of two proposed randomized control trials (“RCTs”) in the legal setting. The first concerns triage of legal services in the context of intimate partner violence prevention. The RCT will deploy a double-randomization scheme to compare results of human (professional) triaging to random triaging. The second study concerns the legal aspects of severe...

9/23/2015- Dean Knox & Christopher Lucas- A Model for Measuring Emotion in Political Speech with Audio Data Wednesday, September 23, 2015

Title: A Model for Measuring Emotion in Political Speech with Audio Data

Abstract: Though we generally assume otherwise, humans communicate using more than bags of words alone. Auditory and visual cues convey important information, such as emotion, in many phenomena of interest of political scientists. However, in part due to the relative difficulty of processing audio data, research has disproportionately focused on the textual component of pre-transcribed corpora. We develop a new hidden Markov model for...

Applied Statistics Workshop Gov 3009

2015

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