David Arbour by darbour

About
Papers

About Me

I am currently a researcher at Adobe research where my work focuses on the intersection between experimentation, causal inference, and machine learning with a particular focus on dependent data. Previously I was a research scientist in the Core Data Science group at Facebook. My work at Facebook focused on developing methods for adaptive experimentation, specifically Bayesian optimization and contextual bandits. I earned a PhD from the College of Information and Computer Sciences of UMass Amherst, where I was advised by David Jensen. My research in grad school focused on methods for causal discovery and inference from observational relational data.

I am currently looking for motivated students for research internships in the Summer of 2025. Interested students should reach out to me directly.

Publications

You can find my Google scholar profile here. It is likely more up to date than what is listed below.

Journals

Conferences

Zeng, Zhenghao, David Arbour, Avi Feller, Raghavendra Addanki, Ryan A. Rossi, Ritwik Sinha, and Edward Kennedy. "Continuous Treatment Effects with Surrogate Outcomes." ICML (2024)
Trivedi, Puja, Ryan A. Rossi, David Arbour, Tong Yu, Franck Dernoncourt, Sungchul Kim, Nedim Lipka, Namyong Park, Nesreen K. Ahmed, and Danai Koutra. "Editing Partially Observable Networks via Graph Diffusion Models." ICML (2024)
Ghadiri, Mehrdad, David Arbour, Tung Mai, Cameron Musco, and Anup B. Rao. "Finite population regression adjustment and non-asymptotic guarantees for treatment effect estimation." NeurIPS (2024)
Ahsan, Ragib, David Arbour, and Elena Zheleva. "Learning relational causal models with cycles through relational acyclification." AAAI (2023)
Maharaj, Akash, Ritwik Sinha, David Arbour, Ian Waudby-Smith, Simon Z. Liu, Moumita Sinha, Raghavendra Addanki, Aaditya Ramdas, Manas Garg, and Viswanathan Swaminathan. "Anytime-valid confidence sequences in an enterprise a/b testing platform." WebConf (2023)
Addanki, Raghavendra, David Arbour, Tung Mai, Cameron Musco, and Anup Rao. "Sample constrained treatment effect estimation." NeurIPS (2022)
Ahsan, Ragib, Zahra Fatemi, David Arbour, and Elena Zheleva. "Non-parametric inference of relational dependence." UAI (2022)
Vinay, Vishwa, Manoj Kilaru, and David Arbour. "Offline Evaluation of Ranked Lists using Parametric Estimation of Propensities." SigIR 2022.
Arbour, David, Drew Dimmery, Tung Mai, and Anup Rao. "Online balanced experimental design." ICML (2022)
Ahsan, Ragib, David Arbour, and Elena Zheleva. "Relational causal models with cycles: representation and reasoning." CLeaR (2022)
Mu, Tong, Georgios Theocharous, David Arbour, and Emma Brunskill. "Constraint sampling reinforcement learning: Incorporating expertise for faster learning." AAAI (2022)
Atrey, Akanksha, Ritwik Sinha, Somdeb Sarkhel, Saayan Mitra, David Arbour, Akash Maharaj, and Prashant Shenoy. "Towards Preserving Server-Side Privacy of On-Device Models." WebConf (2022)
Tanjim, Md Mehrab, Ritwik Sinha, Krishna Kumar Singh, Sridhar Mahadevan, David Arbour, Moumita Sinha, and Garrison W. Cottrell. "Generating and controlling diversity in image search." WACV (2022)
Weld, Galen, Peter West, Maria Glenski, David Arbour, Ryan A. Rossi, and Tim Althoff. "Adjusting for confounders with text: Challenges and an empirical evaluation framework for causal inference." ICWSM (2022)
David Arbour, Drew Dimmery and Arjun Sondhi, "Permutation Weighting". ICML (2021)
David Arbour, Drew Dimmery, Anup Rao, "Efficient Balanced Treatment Assignment for Experimentation". AISTATS (2021)
My Phan, David Arbour, Drew Dimmery, Anup Rao, "Designing Transportable Experiments Under S-admissability". AISTATS (2021)
Eli Sherman, David Arbour, Ilya Shpitser, "General Identification of Dynamic Treatment Regimes Under Interference". AISTATS (2020)
Arjun Sondhi, David Arbour, and Drew Dimmery, "Balanced Off-Policy Evaluation in General Action Spaces". AISTATS (2020)
David Arbour, Dan Garant, David Jensen "Inferring Causal Effects in Relational Data". KDD (2016)
David Arbour, Katerina Marazopoulou, David Jensen, "Inferring Causal Direction from Relational Data". UAI (2016)
Maier, Marc, Katerina Marazopoulou, David Arbour, David Jensen "A sound and complete algorithm for learning causal models from relational data." UAI (2013).

Contact Me

The best way to reach me is by email ([first letter][last name]26@gmail.com).

@misc{arbour2015-win-rHSIC,
 author = {David Arbour and Katerina Marazopoulou and David Jensen},
 title = {Look both ways: Dependence and Direction in Relational Data},
 booktitle = {Workshop on Information in Networks},
 year = {2015}
}

@misc{kmarazo2014-nipsw-groundings,
 author = {Katerina Marazopoulou and David Arbour and David Jensen},
 title = {Refining the Semantics of Social Influence},
 booktitle = {Networks: From Graphs to Rich Data, NIPS Workshops},
 year = {2014}
}

@misc{darbour2014-uai-rpsm,
 author = {David Arbour and Katerina Marazopoulou and Dan Garant and David Jensen},
 title = {Propensity Score Matching for Causal Inference with Relational Data},
 booktitle = {Causal Inference: Learning and Prediction Workshop, UAI},
 year = {2014}
}

@misc{arbour2013agglomerative,
  title={Agglomerative Clustering of Bagged Data Using Joint Distributions},
  author={Arbour, David and Atwood, James and El-Kishky, Ahmed and Jensen, David},
  year={2013},
  booktitle = {Structured Learning: Inferring Graphs from Structured and Unstructured Inputs Workshop, ICML},

}

@misc{maier2013-win-flattening,
 author = {Marc Maier and Katerina Marazopoulou and David Arbour and David Jensen},
 title = {Flattening network data for causal discover: What could go wrong?},
 booktitle = {Workshop on Information in Networks},
 year = {2013}
}

@inproceedings{maier2013rcd,
 author = {Marc Maier and Katerina Marazopoulou and David Arbour and David Jensen},
 title = {A Sound and Complete Algorithm for Learning Causal Models from Relational Data},
 booktitle = {Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence},
 year = {2013}
}

@misc{maier2013-rcd-acsl,
 author = {Marc Maier and Katerina Marazopoulou and David Arbour and David Jensen},
 title = {A Sound and Complete Algorithm for Learning Causal Models from Relational Data},
 booktitle = {Approaches to Causal Structure Learning Workshop, UAI},
 year = {2013}
}

@inproceedings{dickson2010evaluation,
  title={Evaluation of automatic classroom capture for computer science education},
  author={Dickson, Paul E and Arbour, David T and Adrion, W Richards and Gentzel, Amanda},
  booktitle={Proceedings of the fifteenth annual conference on Innovation and technology in computer science education},
  pages={88--92},
  year={2010},
  organization={ACM}
}

@article{dickson2009first,
  title={First experiences with a classroom recording system},
  author={Dickson, Paul E and Adrion, W Richards and Hanson, Allen R and Arbour, David T},
  journal={ACM SIGCSE Bulletin},
  volume={41},
  number={3},
  pages={298--302},
  year={2009},
  publisher={ACM}
}