Arshia Arya
Graduate Research Assistant
San Diego, California, United States
Summary
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Senior🎓
Top SchoolArshia Arya is a PhD student and Graduate Research Assistant at UC San Diego with nine years of experience bridging economics and computer science through research and applied engineering. He has a hybrid background from BITS Pilani (CS + MSc Economics) and a track record of research internships and fellowships at Microsoft and industry placements at Intuit and SENSEI Technologies. His work focuses on causal inference—contributing example-driven notebooks to the widely used DoWhy Python library—and he has collaborated on academic projects with TCS Innovation Labs. Comfortable moving between data engineering, causal modeling, and applied research, he brings both rigorous academic publishing and hands-on implementation experience to complex inference problems. An interesting thread through his career is that he pairs economic intuition with software engineering practice to make causal methods reproducible and accessible.
10 years of coding experience
1 year of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceScience, Science at Delhi Public School, Ranchi
Hindi, English
Github Skills (8)
causal10
pandas10
jupyter-notebook10
causality10
python10
data-science10
causal-inference10
machine-learning9
Programming languages (2)
Github contributions (5)
py-why/dowhy
Feb 2020 - Dec 2020
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
Role in this project:
Data Scientist
Data ScientistContributions:2 reviews, 11 commits, 6 PRs in 10 months
Contributions summary:Arshia primarily contributes to the DoWhy library by adding and modifying example notebooks. These notebooks demonstrate the use of DoWhy for causal inference tasks, specifically analyzing treatment effects using the Twins dataset. The user's work involves loading and preprocessing data, constructing causal models, and implementing refutation techniques to assess the validity of causal estimates. The contributions directly showcase the application of DoWhy's functionalities within the context of causal analysis.
causal-inferencedowhygraphical-modelspythontesting
Arshiaarya/arshiaarya.github.io
Aug 2020 - Sep 2022
Personal website
Contributions:47 pushes in 2 years 1 month
reactnextjs
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