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Tomas Jimenez, Stanford University

4240 Public Affairs Bldg

The Other Side of Assimilation: How Immigrants are Changing American Life

The immigration patterns of the last three decades have profoundly changed nearly every aspect of life in the United States. What do those changes mean for the most established Americans—those whose families have been in the country for multiple generations? The Other Side of Assimilation shows that assimilation is not a one-way street. Jiménez explains how established Americans undergo their own assimilation in response to profound immigration-driven ethnic, racial, political, economic, and cultural shifts.

Co-sponsored with the Center for the Study of International Migration and the Race and Ethnicity Sociology Working Group

Brandon Stewart, Princeton University

CCPR Seminar Room 4240 Public Affairs Building, Los Angeles

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

Susan Athey, Stanford University

CCPR Seminar Room 4240 Public Affairs Building, Los Angeles

Estimating Heterogeneous Treatment Effects and Optimal Treatment Assignment Policies

This talk will review recently developed methods for estimating conditional average treatment effects and optimal treatment assignment policies in experimental and observational studies, including settings with unconfoundedness or instrumental variables. Multi-armed bandits for learning treatment assignment policies will also be considered.

Co-sponsored with the Center for Social Statistics

Latest Past Events

Tomas Jimenez, Stanford University

4240 Public Affairs Bldg

The Other Side of Assimilation: How Immigrants are Changing American Life

The immigration patterns of the last three decades have profoundly changed nearly every aspect of life in the United States. What do those changes mean for the most established Americans—those whose families have been in the country for multiple generations? The Other Side of Assimilation shows that assimilation is not a one-way street. Jiménez explains how established Americans undergo their own assimilation in response to profound immigration-driven ethnic, racial, political, economic, and cultural shifts.

Co-sponsored with the Center for the Study of International Migration and the Race and Ethnicity Sociology Working Group

Brandon Stewart, Princeton University

CCPR Seminar Room 4240 Public Affairs Building, Los Angeles

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

Susan Athey, Stanford University

CCPR Seminar Room 4240 Public Affairs Building, Los Angeles

Estimating Heterogeneous Treatment Effects and Optimal Treatment Assignment Policies

This talk will review recently developed methods for estimating conditional average treatment effects and optimal treatment assignment policies in experimental and observational studies, including settings with unconfoundedness or instrumental variables. Multi-armed bandits for learning treatment assignment policies will also be considered.

Co-sponsored with the Center for Social Statistics

UCLA CCPR