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Jennie E. Brand, Professor of Sociology and Statistics, University of California, Los Angeles.
Individuals do not respond uniformly to treatments, events, or interventions. Social scientists routinely partition samples into subgroups to explore how the effects of treatments vary by covariates like race, gender, and socioeconomic status. In so doing, analysts determine the key subpopulations based on theoretical priors.
Data-driven discoveries are also routine, yet the analyses by which social scientists typically go about them are problematic and seldom move us beyond our expectations, and biases, to explore new meaningful subgroups. Emerging machine learning methods allow researchers to explore sources of variation that they may not have previously considered, or envisaged.
In this paper, we use causal trees to recursively partition the sample and uncover sources of treatment effect heterogeneity. We use honest estimation, splitting the sample into a training sample to grow the tree and an estimation sample to estimate leaf-specific effects. Assessing a central topic in the social inequality literature, college effects on wages, we compare what we learn from conventional approaches for exploring variation in effects to causal trees. Given our use of observational data, we use leaf-specific matching and sensitivity analyses to address confounding and offer interpretations of effects based on observed and unobserved heterogeneity. We encourage researchers to follow similar practices in their work on variation in effects.
About the speaker
Jennie E. Brand is Professor of Sociology and Statistics at the University of California, Los Angeles (UCLA). She is Director of the California Center for Population Research (CCPR) and Co-Director of the Center for Social Statistics (CSS) at UCLA.
Prof. Brand studies social stratification and inequality, mobility, social demography, education, and methods for causal inference. Her current research agenda encompasses three main areas: (1) access to and the impact of higher education; (2) the socioeconomic and social-psychological consequences of disruptive events, such as job displacement; and (3) causal inference and the application and innovation of quantitative methods for panel data.
She is Chair-Elect of the Methodology Section of the American Sociological Association (ASA) and an elected Board Member of the International Sociological Association (ISA) Research Committee on Social Stratification and Mobility (RC28). She was elected to the Sociological Research Association (SRA), an honor society for excellence in research, in 2019.
Prof. Brand is a member of the Board of Overseers of the General Social Survey (GSS) and a member of the Technical Review Committee for the National Longitudinal Surveys Program at the Bureau of Labor Statistics.
She received the ASA Methodology Leo Goodman Mid-Career Award in 2016, and honorable mention for the ASA Inequality, Poverty, and Mobility William Julius Wilson Mid-Career Award in 2014.
Learn more about Jennie E. Brand's work
profjenniebrand.com
