Alexander D'Amour

Neyman Visiting Assistant Professor, Department of Statistics, UC Berkeley

Curriculum Vitae

I am currently the acting Neyman Visiting Assistant Professor in the Department of Statistics at UC Berkeley. I am also a PhD candidate in the Department of Statistics at Harvard University, where I am advised by Edoardo Airoldi. I am a member of the Harvard Laboratory for Applied Statistical Methodology & Data Science.

I work on a wide variety of Statistics and Machine Learning problems with the goal of developing foundational principles for applied statistics and Data Science that unify themes in design, modeling, inference, and decision rules that cut across application areas and methodologies. I am particularly interested in applied problems that involve large, highly dependent systems that push the boundaries of classical statistical methodology -- for example, those that produce network, event, and spatial data.

I am an active member of the XY Research group, which conducts research in sports statistics with a focus on player-tracking data.


Dissertation. My dissertation research is about the statistical analysis of social network data, particularly the logical difficulties that arise from the sparse scaling behavior of social networks. This work spans the full stack arguments and methods that are employed in a scientific investigation of social networks, from the logical role that misspecified models play in an investigation, to new modeling and inference methodologies for drawing predictive and causal inferences from the network data.

Application areas. Through my academic and consulting work, I have completed projects in a wide range of fields, including document disambiguation, text analysis, epidemiology, education technology, digital marketing, customer modeling in e-commerce, and credit access in developing economies.

Background.  I currently hold AB and SM degrees in Applied Mathematics, also from Harvard University.



Misspecification, Sparsity, and Superpopulation Inference for Sparse Social Networks
Theoretical characterization of how the sparse scaling of social networks undermines superpopulation investigations when the sparsity is not modeled exactly. Proposes sparsity-invariant modeling and inference methodology.
Alexander D'Amour and Edoardo Airoldi

Causal Inference with Sparse Social-Interaction-Valued Outcomes
Extension of sparsity-invariant methodology for network data to causal settings.
Alexander D'Amour and Edoardo Airoldi

  • In preparation.

Measuring the Causal Effect of the Michigan Anti-trust Reform Act of 1986 on Inventor Collaboration Dynamics in Michigan
Sparsity-invariant methodology applied in a causal policy evaluation using the US patent coauthorship network. Alexander D'Amour, Edoardo Airoldi, and Lee Fleming

  • In preparation.

The Effective Estimand
A framework for characterizing the scientific usefulness of an estimator derived from a misspecified model. Generalizes the work on networks to general modeling tasks.
Alexander D'Amour and Edoardo Airoldi

To Appear

A Multiresolution Stochastic Process Model for Predicting Basketball Possession Outcomes
Methodology for computing Expected Possession Value, an instantaneous expected point value for a basketball possession.
Daniel Cervone, Alexander D'Amour, Luke Bornn, and Kirk Goldsberry
Journal of the American Statistical Association


Disambiguation and Co-authorship Networks of the U.S. Patent Inventor Database
A supervised learning approach to adding unique inventor identifiers to the US patent database.
G. Li, R. Lai, Alexander D'Amour, D. Doolin, Y. Sun, V. Torvik, A. Yu, and L. Fleming
Research Policy, 2014.

Estimating Rates of Carriage Acquisition and Clearance and Competitive Ability for Pneumococcal Serotypes in Kenya With a Markov Transition Model
Markov model approach to estimating epideiological properties of Pneumococcal serotypes using periodic testing data from Kenyan schoolchildren.
M. Lipsitch, O. Abdullani, Alexander D'Amour, W. Xie, D. Weinberger, E. Tchetgen, and J. Scott
Epidemiology, 2012.

Improving Major League Park Factor Estimates
An ANOVA approach to estimating park factors in Major League Baseball. Written in conjunction with the Harvard Sports Analysis Collective.
R. Acharya, A. Ahmed, Alexander D'Amour, H. Lu, C. Morris, B. Oglevee, A. Peterson, and R. Swift

Talks, Posters, Other Media


Prediction is Not Enough: Designing decision-support statistics for causal inference and attribution
Exploration of Statistical applications where the objective requires more than the ability to predict future replications of the observe data stream.
Invited talk at Lumos Labs in San Francisco, CA.

A Design-Based Perspective on Variable Selection
An approach to variable selection that treats it as the design choice -- namely choosing which conditional distribution to model. Some preliminary thoughts on optimal data-splitting.
Talk given in the Harvard Statistics Department's Research in Statistics student colloquium.


Move or Die: How Ball Movement Creates Open Shots in the NBA
Uses summaries of a Markov model for basketball possessions to show that ball movement is effective only inasmuch as it introduced unpredictability into an NBA offense.
Winner: Best Poster, 2015 Sloan Sports Analytics Conference.

Popular Media

Bayesian Statistician
You're the Expert (radio show)

Behind Databall: A Discussion on the Methodology of Expected Possession Value



At Harvard, I had the opportunity to serve as a teaching fellow for a variety of courses. Duties included running weekly discussion sections, developing coarse material, and giving guest lectures.

  • Statistics 220: Bayesian Data Analysis (Fall 2011, Fall 2012)
  • Statistics 221: Statistical Computation and Visualization (Spring 2013)
  • Statistics 225: Spatial Statistics (Spring 2014)
  • Statistics 121/Computer Science 109: Data Science (Fall 2013, Fall 2014)
  • Statistics 107: Financial Statistics (Spring 2012)



I field many applied statistical problems from industry in an active Data Science consulting practice. I am a founding partner of Damyata, LLC, a consultancy that I founded with two tech industry veterans. Our mission is to establish best practices in Data Science by delivering state-of-the art data-driven systems to our clients. A core part of our mission is to foster academic-industry research partnerships.

Former and current consulting clients include