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April 2019

Jennifer Ahern, UC Berkeley

April 24, 2019 @ 12:00 pm - 1:30 pm PDT
CCPR Seminar Room, 4240 Public Affairs Building
Los Angeles, CA 90095 United States

Leveraging big data to assess health effects of changes in physical and social environments, and policy and program implementation

In the era of big data there are opportunities to answer policy-relevant health questions in ways that are timely and cost-efficient. This talk will describe coordination of health data resources for health monitoring and to address questions about the health effects of policies in California. Examples of health effect assessments, including those related to gun shows and the Mental Health Services Act (MHSA), will be presented.

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Workshop: UCLA IRB Application Process

April 25, 2019 @ 2:30 pm - 3:30 pm PDT

Workshop: UCLA IRB Application Process Presentation by Moore Rhys Assistant Director, Education and Quality, Office of the Human Research Protection Program, UCLA This workshop will provide an overview of the UCLA IRB application process and related policies and procedures. Learning goals for this workshop include: 1)      Understanding when IRB review is required and when it is not 2)      Identifying what training is required for conducting human subject research and how to complete it 3)      Understanding how to get started in…

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May 2019

Susan Athey, Stanford University

May 1, 2019 @ 12:00 pm - 1:30 pm PDT
CCPR Seminar Room, 4240 Public Affairs Building
Los Angeles, CA 90095 United States

Co-sponsored with the Center for Social Statistics

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Brandon Stewart, Princeton University

May 8, 2019 @ 12:00 pm - 1:30 pm PDT
CCPR Seminar Room, 4240 Public Affairs Building
Los Angeles, CA 90095 United States

How to Make Causal Inferences Using Texts

Texts are increasingly used to make causal inferences: either with the document serving as the treatment or the outcome. We introduce a new conceptual framework to understand all text-based causal inferences, demonstrate fundamental problems that arise when using manual or computational approaches applied to text for causal inference, and provide solutions to the problems we raise.  We demonstrate that all text-based causal inferences depend upon a latent representation of the text and we provide a framework to learn the latent representation.  Estimating this latent representation, however, creates new risks: we may unintentionally create a dependency across observations or create opportunities to fish for large effects.  To address these risks, we introduce a train/test split framework and apply it to estimate causal effects from an experiment on immigration attitudes and a study on bureaucratic responsiveness.  Our work provides a rigorous foundation for text-based causal inferences, connecting two previously disparate literatures. (Joint Work with Egami, Fong, Grimmer and Roberts)

Co-sponsored with the Center for Social Statistics

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Pascaline Dupas, Stanford University

May 23, 2019 @ 12:00 pm - 1:30 pm PDT
CCPR Seminar Room, 4240 Public Affairs Building
Los Angeles, CA 90095 United States

The Incidence of Public Subsidies to Private Hospitals under Weak Governance: Evidence from India (joint with Radhika Jain)

Expanding public health insurance and enlisting private agents for service delivery are common policy strategies to meet the goals of universal health coverage, but there is limited evidence from developing countries to inform their design. This paper, joint with Radhika Jain from Harvard School of Public Health, provides quantitative evidence on how insurance design affects program performance and incidence in the context of a government-funded health insurance program that aims to provide free care to 46 million people in Rajasthan, India. We exploit a policy-induced natural experiment, and use administrative claims linked to patient surveys, to provide the first large-scale evidence of private hospital behavior under public health insurance.

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