Data Visualization

Propensity Score Matching for Causal Inference: Creating Data Visualizations to Assess Covariate Balance in R

June 10, 2024
Sharon Green
by Sharon Green. Although some people consider randomized experiments the gold standard, in many cases, it would be highly unethical to assign individuals to harmful exposures to measure their effects. Modern causal inference techniques help scientists to estimate treatment effects using observational data. In particular, propensity score matching helps scientists estimate causal effects using observational data by matching individuals so that the “treatment” and “control” groups are balanced on measured covariates. After implementing propensity score matching, data visualizations make it easier to assess the quality of the matches before estimating effects. This blog post is a tutorial for implementing propensity score matching and creating data visualizations to assess covariate balance–that is, visually assessing whether the matched individuals are balanced with respect to measured covariates.

Transparency in Experimental Political Science Research

April 9, 2024
Kamya Yadav
by Kamya Yadav. With the increase in studies with experiments in political science research, there are concerns about research transparency, particularly around reporting results from studies that contradict or do not find evidence for proposed theories (commonly called “null results”). To encourage publication of results with null results, political scientists have turned to pre-registering their experiments, be it online survey experiments or large-scale experiments conducted in the field. What does pre-registration look like and how can it help during data analysis and publication?

Chirag Manghani

Consultant
School of Information

Chirag is a 2nd year graduate at the I-School. Proficient in Python, Java, R, and SQL, he navigates software application development, machine learning and data science. His keen interest lies in data analysis and statistical methods, driving him to bridge theory and practice seamlessly. Chirag's dedication to excellence, adaptable mindset, and innate curiosity define him as a dynamic problem solver in the ever-evolving tech landscape.

Nicolas Nunez-Sahr

Consultant
Statistics

I lived in Santiago, Chile until I graduated from high school, and then moved to the US for undergrad at Stanford, where I obtained a Bachelor’s degree from the Statistics Department. I then worked as a Data Scientist in an NLP startup that was based in Bend, OR, which analyzed news articles. I love playing soccer, volleyball, table tennis, flute, guitar, latin music, and meeting new people. I want to get better at mountain biking, whitewater kayaking, chess and computer vision. I find nature astounding, and love finding sources of inspiration.

Gaby May Lagunes

Consultant
ESPM

Hello! I’m Gaby (she/her). I am PhD student at the ESPM department, I hold a masters in Data Science and Information from the Berkeley ISchool and I have 5+ years of industrial experience in different data roles. Before that I got a masters in Engineering for International Development and an undergraduate degree in Physics from University College London. And somewhere between all that I got married, survived the pandemic, and had two awesome boys. I’m very excited to help you use data to enhance your work and your experience here at Berkeley!

Thomas Lai

Consultant
School of Information

I am a Product Engineer passionate about applying engineering, data science, machine learning, and problem-solving principles to improve device performance and solve complex challenges. With experience in statistical analysis, lab bench automation, and Python scripting, I have developed a strong technical skill set that allows me to make meaningful contributions to any project. Beyond my work, I am also passionate about exploring new topics and ideas, from the latest technology trends to how to improve the overall well-being of humans. I enjoy applying the first principle to any...

Design Your Observational Study with the Joint Variable Importance Plot

March 12, 2024
Lauren Liao
by Lauren Liao. When evaluating causal inference in observational studies, there often is a natural imbalance in the data. Luckily, variables are often measured alongside that can be helpful for adjustment. However, deciding which variables should be prioritized for adjustment is not trivial – since not all variables are equally important to the intervention or the outcome. I recommend using the joint variable importance plot during the observational study design phase to visualize which variables should be prioritized. This post provides a gentle guide on how to do so and why it is important.

What Are Vowels Made Of? Graphing a Classic Dataset with R

February 13, 2024
Anna Björklund
by Anna Björklund. Vowels are all around us. Mainstream US English has around twelve unique vowels. How can our brains tell these sounds apart? This blog post will help you answer this question by plotting vowel data from a classic American English dataset by Peterson and Barney (1952).

Jailynne Estevez

Consulting Drop-In Hours: Fri 3pm-5pm

Consulting Areas: Python, SQL, Stata, HTML / CSS, Javascript, Google AppScripts, Databases & SQL, Data Manipulation and Cleaning, Data Science, Data Sources, Data Visualization, Python Programming, Surveys, Sampling & Interviews, Text Analysis, , Bash or Command Line, Excel, Git or Github, Stata

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Chirag Manghani

Consulting Drop-In Hours: Wed 1pm-3pm

Consulting Areas: Python, R, SQL, Stata, SAS, LaTeX, HTML / CSS, Javascript, C++, APIs, Cloud & HPC Computing, Cybersecurity & Data Security, Databases & SQL, Data Manipulation and Cleaning, Data Science, Data Sources, Data Visualization, Deep Learning, Machine Learning, Natural Language Processing, Python Programming, R Programming, Software Tools, Text Analysis, Web Scraping, Regression Analysis, Software Output Interpretation, Bash or Command Line, Excel, Git or Github, Qualtrics, RStudio, RStudio...