Presentations

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...

9/16/2015- Gary King (Harvard)- Why Propensity Scores Should Not Be Used for Matching Wednesday, September 16, 2015

Abstract: Researchers use propensity score matching (PSM) as a data preprocessing step to selectively prune units prior to applying a model to estimate a causal effect. The goal of PSM is to reduce imbalance in the chosen pre-treatment covariates between the treated and control groups, thereby reducing the degree of model dependence and potential for bias. We show here that PSM often accomplishes the opposite of what is intended -- increasing imbalance, inefficiency, model dependence, and bias. The weakness of PSM is that it attempts to approximate...

9/9/2015- Matthew Blackwell (Harvard)- Identification and Estimation of Joint Treatment Effects with Instrumental Variables Wednesday, September 9, 2015

Title: Identification and Estimation of Joint Treatment Effects with Instrumental Variables

Abstract- Over the last twenty years, a literature spanning several fields of applied statistics has analyzed how to identify and estimate causal effects of a nonrandomized treatment when a instrumental variable (IV) is available. But researchers often have multiple treatments and want to estimate either the direct or joint effect of these treatments. This paper introduces a set of novel estimands for instrumental variables with multiple treatments and multiple...

Finale Doshi-Velez (Harvard) - Bayesian Or-of-And Models for Interpretable Classification Wednesday, April 29, 2015

Abstract: Interpretability is an important factor for models to be used and trusted in many applications. Disjunctive normal forms, also known as or-of-and models, are models with classification rules of the form "Predict True if (A and B) or (A and C) or D." They are an appealing form of classifier because one can easily trace how a classification decision was made, and has some basis in human decision-making. In this talk, I will talk about a Bayesian approach to learning or-of-and models and describe an application to context-aware...

Neil Shephard (Harvard)- Pricing each income contingent student loan using administrative data. Some statistical challenges Wednesday, April 22, 2015

Abstract: Income student loans are used in many countries as the prime way for students to fund their tuition fees and maintenance. Repayments on the loans are a fraction of the former student’s income above some threshold. In England the fraction is 9% and the threshold is around $35,000. Interest is charged on the loans and any outstanding debt is forgiven after 30 years. The UK Government has offered to issue such loans to any qualified English student going to a UK university since 1998. How much are these loans worth to the...

Tyler VanderWeele (Harvard) - A Unification of Mediation and Interaction: A 4-Way Decomposition Wednesday, April 15, 2015

Abstract: The overall effect of an exposure on an outcome, in the presence of a mediator with which the exposure may interact, can be decomposed into 4 components: (1) the effect of the exposure in the absence of the mediator, (2) the interactive effect when the mediator is left to what it would be in the absence of exposure, (3) a mediated interaction, and (4) a pure mediated effect. These 4 components, respectively, correspond to the portion of the effect that is due to neither mediation nor interaction, to just interaction (but not mediation), to both...

Applied Statistics Workshop Gov 3009

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