Background Info

Name: Arnav Gurudatt
Pronouns: he/him
Year: 2025
Majors: Data Science and Economics, minor in Stats
Hometown: San Mateo, CA
Hobbies/interests: econometrics, basketball, Pokemon Showdown
Position: RP Director

Past/Current Committees

Fall 2021: RP Member
Spring 2022: RP Member
Fall 2022: RP Director
Spring 2023: RP Director

Why did you choose data science and economics as your majors with a minor in statistics?

My main area of interest in economics is econometrics, which is the idea of using a bunch of statistical tools to answer inferential or causal questions about how the world works or policies that you implement. I think it’s very interesting, the idea that if a state or government passes a policy, you can use an array of statistical techniques to causally assess whether something had a direct impact on the people it was meant to help. So I thought it was a very powerful thing and it made me want to learn more about the statistical and inferential tools that econometricians or statisticians use to answer those questions. Data science and statistics as well are good ways to pursue that. As I’ve come to learn, there’s kind of a divide between data science and econometrics and the questions they’re meant to answer. Econometrics is focused more on inference questions, like can we estimate the effect size of a certain policy? Or can we estimate the effect size of a program we implement? But I think data science is focused more on prediction-related questions, so like can we predict whether an email is spam or not and make a spam filter? So those are two very different questions but at their intersection they have some cool applications, which I’m interested in exploring.

What specifically are you interested in professionally in the applications of econometrics?

Even I’m not fully sure of what I want to do, but some of my past work experience has been working with Washington DC policy advocacy groups and doing econometrics research for them. I worked for an organization that studies tax policy as a research intern last summer, and I did a lot of micro- and macro-simulation of various tax policies and how they’d affect things like returns to capital, labor supply, and things like that. Right now I’m working on conducting research with an organization that studies universal basic income and cash transfers, which is interesting because a lot of that research is more focused on randomized controlled trials. It makes econometrics easier because it’s much easier to identify causal outcomes when the data you have is generated through randomization to begin with. I think in the long term, I’d probably want to go more towards business analytics or working on causal inference or experimentation in industry. Causal inference is my biggest area of interest, and I think it’d be cool to study in some way, shape, or form in grad school whether that’s through statistics, economics, or operations research.

How did you find out about SAAS, and why did you join?

I found out about SAAS through tabling. I was walking to class, and someone screamed, “Interested in data science?” I said sure, scooped up the flier, and applied. That’s the entire story. I didn’t do any coffee chats or info-sessions—I just applied, did the interview, and got into SAAS. It’s a very dry story but that is what happened. I joke and say that it’s because the SAAS colors were really pretty, which isn’t the entirety of why I applied, but it certainly helped that I liked the pink and blue color scheme.

What is your proudest accomplishment in SAAS?

My proudest accomplishment was my Spring 2022 Research and Publication. I used a new-ish method in causal inference called the “synthetic control method” to analyze the effects of a health insurance expansion that was done in Massachusetts in 2006. So the way this program worked was Massachusetts expanded eligibility for public insurance, which was through their state-run Medicaid program. They also made it easier to purchase private insurance through a common market and passed a law mandating that everyone buy health insurance of some kind, with the idea being that everyone would be on a healthcare plan and they could choose whether it’d be public or private. The tricky part with analyzing the causal effect of such a policy on healthcare coverage and healthcare outcomes is that there's no control group in Massachusetts. You can’t randomly assign people healthcare for various ethical and logistical reasons, so ascertaining causal effects is very difficult. But a bunch of smart people at MIT figured out that if you just treat the rest of the United States as the control group, because other states didn’t implement the policy, and then use various optimization techniques to create a “synthetic Massachusetts,” which is a linear combination of untreated states, you can actually estimate what would have happened in the absence of the policy to really great accuracy. That’s the idea behind synthetic control, and I’m really proud of this project because it’s very technical, it’s something I’m very interested in and learned a lot about causal inference doing, and my project won a top prize at the Symposium from our sponsor.

What is a core SAAS memory for you?

At banquet in Fall 2022—that was the first SAAS banquet I’d ever been to. I was sick the previous two semesters so I couldn’t make it. But at this banquet, I rapped my committee presentation. My co-director Isabella and I wrote a rap in 30 minutes that talked about everyone in our committee’s research projects. The funny story behind this was that at first, our presentation was really dry and we just talked about everyone’s projects. We had already submitted our presentation to Exec and had them approve a very boring version of it. But we decided at the very last minute to rap it instead. We spent the next few days slapping absurd lyrics together and making a very stupid meme video to go along with it. It was very fun, and I think people enjoyed it. Definitely a core memory.

What is the best advice you’ve ever received?

It’s from my mother. During my first semester at Berkeley, I was very stressed out about grades, like doing well in school, and I was very stressed about a couple of midterms. I called my mom about it, and she told me very simply, “Whatever will be will be.” She basically said that everything is already done and there’s no need to worry about things that are out of my control. I’ve really taken that in stride at Berkeley in these four semesters. Her whole advice to me was that as long as I try my best, there’s no need to worry about what the actual outcome will be.

Why have you stayed in SAAS?

During my first two semesters in SAAS, I appreciated the career development and learning about data science as a first year. That was very valuable to me, as somebody who didn’t have that much experience with data science coming into Berkeley, to just learn about various inferential or statistical tools in the context of research, which is something I’m obviously very interested in. But I think that the biggest thing that’s made me stay in SAAS is that, since joining leadership, I’ve been able to come out of my shell a lot more and found a strong, vibrant community—some of my best friends at Berkeley I’ve met through SAAS. The community and friends I have here are things I’d never want to trade, so that’s why I’m excited to continue to be a part of SAAS.

What’s your favorite compliment to receive?

I like hearing that I’m endearing.

What is a core SAAS memory for you?

At the banquet in Fall 2022—that was the first SAAS banquet I’d ever been to. I was sick the previous two semesters, so I couldn’t make it. But at this banquet, I rapped my committee presentation. My co-director Isabella and I wrote a rap in 30 minutes that talked about everyone in our committee’s research projects. The funny story behind this was that at first, our presentation was really dry, and we just talked about everyone’s projects. We had already submitted our presentation to Exec and had them approve a very boring version of it. But we decided at the very last minute to rap it instead. We spent the next few days slapping absurd lyrics together and making a very stupid meme video to go along with it. It was very fun, and I think people enjoyed it. Definitely a core memory.

If money didn’t matter, what would you do with your life?

Honest to God, I would just never work. Ever. A lot of people might choose a different career that’s really interesting and really enjoy it. But no, I just wouldn’t work. I’d sit at home all day and do nothing. But if I had to work and do something to be a contributing member of society, I would probably teach maybe high school or something. I’d let my students push me over and do whatever they want. I’d be a cool teacher. If they wanted to submit an assignment late, I’d let them. I want to be a teacher that I personally would’ve wanted to have, which is a pushover teacher.