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Causal Inference from Observational Data

This week-long course offers an applied introduction to causal inference for social scientists. The course is structured around four key topics: Modern causal notation (potential outcomes and directed acyclic graphs [DAGs]), propensity score matching, instrumental variables, and inference for time-varying treatments.

Democratizing Our Data

Please join us on August 26 from 10am-11am for “Democratizing Our Data,” a lecture by Julia Lane, Professor at the NYU Wagner Graduate School of Public Service, at the NYU Center for Urban Science and Progress, and a NYU Provostial Fellow for Innovation Analytics.Co-sponsored by 

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D-Lab Graduate Student Hiring Info Session

D-Lab is recruiting UC Berkeley graduate students for workshop instructors, consultants, and our D-Lab Data Science Fellows program. We are holding an info session on Thursday, July 22 from 12pm-1pm for folks to learn more about D-Lab and hear from current D-Lab students. We are accepting applications for our open positions through July 19, 2021. Once we receive your application, we will follow up with an invite to the info session. 

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R Bootcamp: Fall 2021

Co-sponsored by the UC Berkeley Statistics Department and the D-Lab.

The workshop will be an intensive two-day introduction to R using RStudio. After the first morning session, the workshop will (staffing permitting) be split into two separate tracks. Topics will include

Machine Learning in Poverty Measurement

Posted: May, 11, 2021

By: Cheng Ren

According to The Sustainable Development Goals (SDG) from the United Nations, the first goal is to "end poverty in all its forms everywhere". However, a common method to measure poverty is census data or large sample research, which collects data from a large sample size. The cost for conducting these researches is even higher in low-income areas due to the scarce infrastructure (Blumenstock, 2016; Jean et al., 2016; Perez et al., 2019, McBride&Nichols, 2015).

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Handling Missing Data

Posted: May, 04, 2021

By: J. Brooks Jessup

I recently started working with a set of eviction data for a project on housing precarity at the Urban Displacement Project. As I began exploring the dataset, I was excited to find that it appeared to contain a wealth of historical data we could use to train a robust model for predicting eviction rates in urban neighborhoods. However, my initial excitement soon had to be scaled back when a standard check for missing data revealed that many of the observations lacked values for precisely the variable we aimed to predict.

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Computational Text Analysis: Returning to Foster Care

Computational Text Analysis Working Group (CTAWG) 

Title: Returning to Foster Care: Revisited with Computational Text Analysis 

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Computational Text Analysis: [UCSF Bakar] Enhancing physicians’ prognoses using deep learning

Computational Text Analysis Working Group (CTAWG)

Title: [UCSF Bakar] Enhancing physicians’ prognoses using deep learning: an ergonomic UI to find similar patient groups and medical trends

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