Alexander Volfovsky

Conference Proceedings of machine learning research · July 2024 Many populations defined by illegal or stigmatized behavior are difficult to sample using conventional survey methodology. Respondent Driven Sampling (RDS) is a participant referral process frequently employed in this context to collect information. This s ... Cite

Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference

Conference Proceedings of the Aaai Conference on Artificial Intelligence · March 25, 2024 After a person is arrested and charged with a crime, they may be released on bail and required to participate in a community supervision program while awaiting trial. These 'pretrial programs' are common throughout the United States, but very little resear ... Full text Cite

Safe and Interpretable Estimation of Optimal Treatment Regimes

Conference Proceedings of Machine Learning Research · January 1, 2024 Recent advancements in statistical and reinforcement learning methods have contributed to superior patient care strategies. However, these methods face substantial challenges in high-stakes contexts, including missing data, stochasticity, and the need for ... Cite

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data

Conference Proceedings of Machine Learning Research · January 1, 2024 Many modern causal questions ask how treatments affect complex outcomes that are measured using wearable devices and sensors. Current analysis approaches require summarizing these data into scalar statistics (e.g., the mean), but these summaries can be mis ... Cite

Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference

Conference Proceedings of Machine Learning Research · January 1, 2020 We propose a matching method that recovers direct treatment effects from randomized experiments where units are connected in an observed network, and units that share edges can potentially influence each others' outcomes. Traditional treatment effect estim ... Cite

Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation

Conference Proceedings of Machine Learning Research · January 1, 2020 We propose a matching method for observational data that matches units with others in unit-specific, hyper-box-shaped regions of the covariate space. These regions are large enough that many matches are created for each unit and small enough that the treat ... Cite

SMOGS: Social Network Metrics of Game Success

Conference Proceedings of Machine Learning Research · January 1, 2019 In this paper we propose a novel metric of bas-ketball game success, derived from a team's dynamic social network of game play. We combine ideas from random effects models for network links with taking a multi-resolution stochastic process approach to mode ... Cite

Interpretable almost-matching-exactly with instrumental variables

Conference 35th Conference on Uncertainty in Artificial Intelligence Uai 2019 · January 1, 2019 Uncertainty in the estimation of the causal effect in observational studies is often due to unmeasured confounding, i.e., the presence of unobserved covariates linking treatments and outcomes. Instrumental Variables (IV) are commonly used to reduce the eff ... Cite