I am currently a researcher at Adobe research where my work focuses on the intersection between experimentation, causal inference & discovery, 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 advanced 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 inference from observational relational data.
You can find my Google scholar profile Publicationshere. It is likely more up to date than what is listed below.
- 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).
- Ryan A. Rossi, Nesreen K. Ahmed, Aldo Carranza, David Arbour, Anup Rao, Sungchul Kim, Eunyee Koh."Heterogenous Graphlets", MLG KDD (2019)
- Katerina Marazopoulou, David Arbour, David Jensen “On causal analysis for heterogeneous networks”. The 2017 ACM SIGKDD Workshop on Causal Discovery.
- David Arbour, Katerina Marazopoulou, David Jensen “Look Both Ways: Dependence and Direction in Relational Data”. 2015 Workshop on Information Networks.
- David Arbour, Katerina Marazopoulou, Dan Garant, David Jensen “Relational Propensity Score Matching”. 2014 UAI Workshop, Causal Inference: Learning and Prediction.
- Katerina Marazopoulou, David Arbour, David Jensen "Refining the Semantics of Social Influence". 2014 NIPS Workshop, Networks: From Graphs to Rich Data.
- Marc Maier, Katerina Marazopoulou, David Arbour, David Jensen "Flattening Network Data for Causal Discovery: What Could Go Wrong?". 2013 Workshop on Information Networks.
- David Arbour, James Atwood, Ahmed El-Kishky, David Jensen "Agglomerative Clustering of Bagged Data using Joint Distributions". 2013 ICML Workshop, Structured Learning: Inferring
Graphs from Structured and Unstructured Inputs.
- Dickson, Paul E., David Arbour, W. Richards Adrion, Amanda Gentzel "Evaluation of automatic classroom capture for computer science education." Proceedings of the fifteenth annual conference on Innovation and technology in computer science education. (2010)
- PE Dickson, WR Adrion, AR Hanson, DT Arbour, "First experiences with a classroom recording system". ACM SIGSCE (2009)
Talks and Poster Presentations
“SoftBlock: Efficient and Optimal Treatment Assignment for Experiments”
2019 Conference on Digital Experimentation
2019 Joint Statistical Meetings (JSM)
2019 Atlantic Causal Infernence Conference
“Look Both Ways: Dependence and Direction in Relational Data”.
2015 Workshop on Information Networks.
“Relational Propensity Score Matching”.
2014 UAI Workshop, Causal Inference: Learning and Prediction.
- AAAI 2019-2021
- AISTATS 2019-2021
- ICLR 2019-2021
- ICML 2019-2021
- ICWSM 2019-2021
- NeurIPS 2016, 2018-2021
- UAI 2017-2021
- WSDM 2021 (Outstanding Reviewer Award)
- WWW 2017, 2018
The best way to reach me is by email ([first letter][last name]firstname.lastname@example.org).
You can also give me a shout on one of these: